Artykuły w czasopismach: „Voice identification”

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Hammarstrom, C. "Voice Identification". Australian Journal of Forensic Sciences 19, nr 3 (marzec 1987): 95–99. http://dx.doi.org/10.1080/00450618709410271.

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Plante-Hébert, Julien, Victor J. Boucher i Boutheina Jemel. "The processing of intimately familiar and unfamiliar voices: Specific neural responses of speaker recognition and identification". PLOS ONE 16, nr 4 (16.04.2021): e0250214. http://dx.doi.org/10.1371/journal.pone.0250214.

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Research has repeatedly shown that familiar and unfamiliar voices elicit different neural responses. But it has also been suggested that different neural correlates associate with the feeling of having heard a voice and knowing who the voice represents. The terminology used to designate these varying responses remains vague, creating a degree of confusion in the literature. Additionally, terms serving to designate tasks of voice discrimination, voice recognition, and speaker identification are often inconsistent creating further ambiguities. The present study used event-related potentials (ERPs) to clarify the difference between responses to 1) unknown voices, 2) trained-to-familiar voices as speech stimuli are repeatedly presented, and 3) intimately familiar voices. In an experiment, 13 participants listened to repeated utterances recorded from 12 speakers. Only one of the 12 voices was intimately familiar to a participant, whereas the remaining 11 voices were unfamiliar. The frequency of presentation of these 11 unfamiliar voices varied with only one being frequently presented (the trained-to-familiar voice). ERP analyses revealed different responses for intimately familiar and unfamiliar voices in two distinct time windows (P2 between 200–250 ms and a late positive component, LPC, between 450–850 ms post-onset) with late responses occurring only for intimately familiar voices. The LPC present sustained shifts, and short-time ERP components appear to reflect an early recognition stage. The trained voice equally elicited distinct responses, compared to rarely heard voices, but these occurred in a third time window (N250 between 300–350 ms post-onset). Overall, the timing of responses suggests that the processing of intimately familiar voices operates in two distinct steps of voice recognition, marked by a P2 on right centro-frontal sites, and speaker identification marked by an LPC component. The recognition of frequently heard voices entails an independent recognition process marked by a differential N250. Based on the present results and previous observations, it is proposed that there is a need to distinguish between processes of voice “recognition” and “identification”. The present study also specifies test conditions serving to reveal this distinction in neural responses, one of which bears on the length of speech stimuli given the late responses associated with voice identification.

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Adhyke, Yuzy Prila, Anis Eliyana, Ahmad Rizki Sridadi, Dina Fitriasia Septiarini i Aisha Anwar. "Hear Me Out! This Is My Idea: Transformational Leadership, Proactive Personality and Relational Identification". SAGE Open 13, nr 1 (styczeń 2023): 215824402211458. http://dx.doi.org/10.1177/21582440221145869.

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This study proposes that there is relationship between transformational leadership and employee’s voice as well as relational identification as a mediation and proactive personality as a moderator. Structural Equation Modeling was used to analyze data gathered from employees at the Ministry of Law and Human Rights through questionnaires. The findings revealed that transformational leadership has a significant effect on employee’s voice and relational identification; relational identification mediates the relation between transformational leadership and employee voice behavior, and proactive personality will weaken the transformational effect on employee’s voice behavior. This study enriches empirical studies that employee’s voice can represent the opinions and ideas of employees with the presence of relational identification, proactive personality, and transformational leadership in the organization. Furthermore, transformational leadership can build relational identification that is strengthened by a proactive personality so that employees are happy to convey their voices.

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McGorrery, Paul Gordon, i Marilyn McMahon. "A fair ‘hearing’". International Journal of Evidence & Proof 21, nr 3 (17.02.2017): 262–86. http://dx.doi.org/10.1177/1365712717690753.

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Voice identification evidence, identifying an offender by the sound of their voice, is sometimes the only means of identifying someone who has committed a crime. Auditory memory is, however, associated with poorer performance than visual memory, and is subject to distinctive sources of unreliability. Consequently, it is important for investigating authorities to adopt appropriate strategies when dealing with voice identification, particularly when the identification involves a voice previously unknown to the witness. Appropriate voice identification parades conducted by police can offer an otherwise unavailable means of identifying the offender. This article suggests some ‘best practice’ techniques for voice identification parades and then, using reported Australian criminal cases as case studies, evaluates voice identification parade procedures used by police. Overall, we argue that the case studies reveal practices that are inconsistent with current scientific understandings about auditory memory and voice identifications, and that courts are insufficiently attending to the problems associated with this evidence.

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Sabir, Brahim, Fatima Rouda, Yassine Khazri, Bouzekri Touri i Mohamed Moussetad. "Improved Algorithm for Pathological and Normal Voices Identification". International Journal of Electrical and Computer Engineering (IJECE) 7, nr 1 (1.02.2017): 238. http://dx.doi.org/10.11591/ijece.v7i1.pp238-243.

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There are a lot of papers on automatic classification between normal and pathological voices, but they have the lack in the degree of severity estimation of the identified voice disorders. Building a model of pathological and normal voices identification, that can also evaluate the degree of severity of the identified voice disorders among students. In the present work, we present an automatic classifier using acoustical measurements on registered sustained vowels /a/ and pattern recognition tools based on neural networks. The training set was done by classifying students’ recorded voices based on threshold from the literature. We retrieve the pitch, jitter, shimmer and harmonic-to-noise ratio values of the speech utterance /a/, which constitute the input vector of the neural network. The degree of severity is estimated to evaluate how the parameters are far from the standard values based on the percent of normal and pathological values. In this work, the base data used for testing the proposed algorithm of the neural network is formed by healthy and pathological voices from German database of voice disorders. The performance of the proposed algorithm is evaluated in a term of the accuracy (97.9%), sensitivity (1.6%), and specificity (95.1%). The classification rate is 90% for normal class and 95% for pathological class.

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R Hanji, Bhagyashri, Sanjay T. J, Shivam Upadhyay, Tarun M i Yashwanthgowda H. R. "Voice Grounded Gender Identification". Journal of Web Development and Web Designing 05, nr 02 (1.07.2020): 20–25. http://dx.doi.org/10.46610/jowdwd.2020.v05i02.004.

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Fujimoto, Junichiroh. "Identification of voice pattern". Journal of the Acoustical Society of America 94, nr 6 (grudzień 1993): 3539. http://dx.doi.org/10.1121/1.407114.

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Ladefoged, Peter. "Validity of voice identification". Journal of the Acoustical Society of America 114, nr 4 (październik 2003): 2403. http://dx.doi.org/10.1121/1.4778312.

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Kilgore, Ryan, i Mark Chignell. "Simple Visualizations Enhance Speaker Identification when Listening to Spatialized Voices". Proceedings of the Human Factors and Ergonomics Society Annual Meeting 49, nr 4 (wrzesień 2005): 615–18. http://dx.doi.org/10.1177/154193120504900403.

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Spatial audio has been demonstrated to enhance performance in a variety of listening tasks. The utility of visually reinforcing spatialized audio with depictions of voice locations in collaborative applications, however, has been questioned. In this experiment, we compared the accuracy, response time, confidence in task performance, and subjective mental workload of 18 participants in a voice-identification task under three different display conditions: 1) traditional mono audio; 2) spatial audio; 3) spatial audio with a visual representation of voice locations. Each format was investigated using four and eight unique stimuli voices. Results showed greater voice-identification accuracy for the spatial-plus-visual format than for the spatialand mono-only formats, and that visualization benefits increased with voice number. Spatialization was also found to increase confidence in task performance. Response time and mental workload remained unchanged across display conditions. These results indicate visualizations may benefit users of large, unfamiliar audio spaces.

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Liang, Tsang-Lang, Hsueh-Feng Chang, Ming-Hsiang Ko i Chih-Wei Lin. "Transformational leadership and employee voices in the hospitality industry". International Journal of Contemporary Hospitality Management 29, nr 1 (9.01.2017): 374–92. http://dx.doi.org/10.1108/ijchm-07-2015-0364.

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Purpose This study aims to explore the relationship between transformational leadership and employee voice behavior and the role of relational identification and work engagement as mediators in the same. Design/methodology/approach This study uses structural equation modeling to analyze the data from a questionnaire survey of 251 Taiwanese hospitality industry employees. Findings The findings demonstrate that transformational leadership has significant relationships with relational identification, work engagement and employee voice behavior and that relational identification and work engagement sequentially mediate between transformational leadership and employee voice behavior. Practical implications The results of this study provide insights into the intervening mechanisms linking leaders’ behavior with employees’ voices, while also highlighting the potential importance of relational identification in organizations, especially concerning the enhancement of employees’ work engagement and voice. Originality/value The findings reveal the mechanisms by which supervisors’ transformational leadership encourages employees to voice their suggestions, providing empirical evidence of the sequential mediation of relational identification and work engagement. The results help clarify the psychological process by which leaders influence their followers.

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Xuetong, Bai, i Wang Qiuyun. "Challenges of DeepFake Voice to Voice Forensic Identification". Criminal Justice Science & Governance 2, nr 2 (2021): 86–92. http://dx.doi.org/10.35534/cjsg.0202012.

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Lee, Bum Joo, Dong Uk Cho i Yeon Man Jeong. "Identification of Voice Features for Recently Voice Fishing by Voice Analysis". Journal of Korean Institute of Communications and Information Sciences 41, nr 10 (31.10.2016): 1276–83. http://dx.doi.org/10.7840/kics.2016.41.10.1276.

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Et. al., Manasi Bendale,. "Voice Based Disease Identification System". Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, nr 1S (11.04.2021): 96–105. http://dx.doi.org/10.17762/turcomat.v12i1s.1568.

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Human voice as well as the sound of the body is used as a clinical method to assess the health condition of an individual. The evaluation of the human voice has risen as a critical field of exploration. Speech analysis fundamentally involves the extraction of certain features from voice signals for generation of voice in alluring pertinence by using reasonable techniques. This paper brings up normal ailments that sway understanding voice patterns in proof for driving research that have affirmed voice modifications as demonstrative manifestations in their respective ailments and also the technique by which voice analysis can be done.

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Ye, Yongchao, Lingjie Lao, Diqun Yan i Rangding Wang. "Identification of Weakly Pitch-Shifted Voice Based on Convolutional Neural Network". International Journal of Digital Multimedia Broadcasting 2020 (6.01.2020): 1–10. http://dx.doi.org/10.1155/2020/8927031.

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Pitch shifting is a common voice editing technique in which the original pitch of a digital voice is raised or lowered. It is likely to be abused by the malicious attacker to conceal his/her true identity. Existing forensic detection methods are no longer effective for weakly pitch-shifted voice. In this paper, we proposed a convolutional neural network (CNN) to detect not only strongly pitch-shifted voice but also weakly pitch-shifted voice of which the shifting factor is less than ±4 semitones. Specifically, linear frequency cepstral coefficients (LFCC) computed from power spectrums are considered and their dynamic coefficients are extracted as the discriminative features. And the CNN model is carefully designed with particular attention to the input feature map, the activation function and the network topology. We evaluated the algorithm on voices from two datasets with three pitch shifting software. Extensive results show that the algorithm achieves high detection rates for both binary and multiple classifications.

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Harnsberger, James, i Harry Hollien. "Selection of speech/voice vectors in forensic voice identification". Journal of the Acoustical Society of America 130, nr 4 (październik 2011): 2546. http://dx.doi.org/10.1121/1.3655179.

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., Li Jing, Huang Hua . i Liu Sufang . "Voice Identification Based on HMMs". Trends in Applied Sciences Research 1, nr 1 (1.01.2006): 79–82. http://dx.doi.org/10.3923/tasr.2006.79.82.

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Engelberg, Saidoff i Israeli. "Voice identification through spectral analysis". IEEE Instrumentation and Measurement Magazine 9, nr 6 (październik 2006): 52–55. http://dx.doi.org/10.1109/mim.2006.1708353.

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Didla, Grace S., i Dr Harry Hollien. "Voice disguise and speaker identification". Journal of the Acoustical Society of America 138, nr 3 (wrzesień 2015): 1808. http://dx.doi.org/10.1121/1.4933738.

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Boë, Louis-Jean. "Forensic voice identification in France". Speech Communication 31, nr 2-3 (czerwiec 2000): 205–24. http://dx.doi.org/10.1016/s0167-6393(99)00079-5.

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Yarmey, A. Daniel. "Voice Identification Over the Telephone1". Journal of Applied Social Psychology 21, nr 22 (listopad 1991): 1868–76. http://dx.doi.org/10.1111/j.1559-1816.1991.tb00510.x.

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Bohan, Thomas L. "Review of: Forensic Voice Identification". Journal of Forensic Sciences 49, nr 4 (2004): 1–2. http://dx.doi.org/10.1520/jfs2004083.

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Yarmey, A. Daniel, i Eva Matthys. "Voice identification of an abductor". Applied Cognitive Psychology 6, nr 5 (wrzesień 1992): 367–77. http://dx.doi.org/10.1002/acp.2350060502.

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Shigeno, Sumi. "Speaking with a Happy Voice Makes You Sound Younger". International Journal of Psychological Studies 8, nr 4 (25.10.2016): 71. http://dx.doi.org/10.5539/ijps.v8n4p71.

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<p>This study investigates the effects of emotional voices (expressing neutral emotion, sadness, and happiness) on a judgement of a speaker’s age. An experiment was conducted to explore whether happy voices sound younger than neutral and sad voices. The identification of 24 speakers’ ages (12 of each gender) based on their emotional voices was done by 40 participants. The speakers’ ages were 24-75 years. Participants identified the age of each speaker only by hearing his/her emotional voice. The results showed that when a speaker spoke with a happy voice, participants estimated their age to be younger than their chronological age. Furthermore, the results regarding female happy voices were more conspicuous than male happy voices. In contrast, when a speaker spoke with a sad voice, participants estimated them to be older. The results suggest that a happy voice sounds younger because of its higher voice pitch (<em>F0</em>). We discussed the role of vocal pitch and other paralinguistic factors for providing an aging impression.</p>

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Kyrychenko, Oleh, i Oleksandr Khrystov. "Organisational-tactical peculiarities of using public representatives in the course of presentation for voice or speech recognition". Collection of Ukrainian Research Institute of Special Equipment and Forensic Expertise of the Security Service OF Ukraine, nr 1 (28.01.2021): 33–44. http://dx.doi.org/10.54658/ssu.27097978.2021.1.pp.33-44.

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The article has identified the main organizational, tactical and legal issues of using members of the public during the presentation for recognition by voice or speech, including phonogram. The authors have concluded that the most effective in terms of organizational implementation of this investigative (search) action, in terms of involvement of mutes, experts, witnesses, there is always lack of cooperation with NGOs, volunteers, representatives of labor collectives and cultural community. They have developed and proposed algorithms for using members of the public as: 1) persons who are presented together with the suspect as «mutes» for identification by «live» voice; 2) persons who perform the role of a source for the creation of audio samples of the voice (when recognizing by phonogram); 3)professionals to create audio samples of the voice (when recognizing by phonogram); 4) professionals to establish possible changes in the voice of the person to be identified; 5) witnesses involved in order to certify the correctness and objectivity of the recording of the content, course and results of the presentation for recognition by voice or speech. For this purpose typical investigative situations that arise in the case of the use of members of the public during the presentation for identification by voice and speech have been identified, in particular: 1. In the use of members of the public as persons who are presented together with the suspect as «extras» for identification by a «live» voice, typical investigative situations include: 1) discrepancy between the characteristics of the voice (volume, intelligibility of speech, timbre, gender of the person, etc.) of colleagues or persons who are in «close access» and the characteristics of the person’s voice presented for identification; 2) refusal of colleagues or persons who are in «close access» to participate in this investigative (search) action as a mute, etc.; 3) the investigator’s inability to determine the characteristics of mutes’ voices at the stage of preparation for the investigative (search) action. 2. When using members of the public as persons who act as a source for the creation of audio samples of the voice (when recognizing by phonogram), the authors have identified the following typical investigative situations: 1) the sound of the created phonogram (signal) does not correspond to «living» voices, or is perceived differently; 2) the number of pronounced words and phrases or their sequence is insufficient for recognition by the recognizing person, etc. 3. When using members of the public as specialists to create audio samples of the voice (when recognizing by phonogram), the following tactical situations may arise: 1) lack of opportunity to involve an expert; 2) conducting an investigative (search) action in remote settlements.

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BIRCHWOOD, M., A. MEADEN, P. TROWER, P. GILBERT i J. PLAISTOW. "The power and omnipotence of voices: subordination and entrapment by voices and significant others". Psychological Medicine 30, nr 2 (marzec 2000): 337–44. http://dx.doi.org/10.1017/s0033291799001828.

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Background. Cognitive therapy for psychotic symptoms often embraces self-evaluative beliefs (e.g. self-worth) but whether and how such beliefs are related to delusions remains uncertain. In previous research we demonstrated that distress arising from voices was linked to beliefs about voices and not voice content alone. In this study we examine whether the relationship with the voice is a paradigm of social relationships in general, using a new framework of social cognition, ‘ranking’ theory.Method. In a sample of 59 voice hearers, measures of power and social rank difference between voice and voice hearer are taken in addition to parallel measures of power and rank in wider social relationships.Results. As predicted, subordination to voices was closely linked to subordination and marginalization in other social relationships. This was not the result of a mood-linked appraisal. Distress arising from voices was linked not to voice characteristics but social and interpersonal cognition.Conclusion. This study suggests that the power imbalance between the individual and his persecutor(s) may have origins in an appraisal by the individual of his social rank and sense of group identification and belonging. The results also raise the possibility that the appraisal of voice frequency and volume are the result of the appraisal of voices' rank and power. Theoretical and novel treatment implications are discussed.

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Rácz, József, Zsuzsa Kaló, Szilvia Kassai, Márta Kiss i Judit Nóra Pintér. "The experience of voice hearing and the role of self-help group: An interpretative phenomenological analysis". International Journal of Social Psychiatry 63, nr 4 (27.03.2017): 307–13. http://dx.doi.org/10.1177/0020764017700926.

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Background: Auditory verbal hallucinations (AVHs) played an important role in the psychiatric diagnostics, but in the last few decades the diagnostic-free complex phenomenological understanding of the phenomena of voice hearing became the focus of studies. Materials: Six semi-structured interviews with recovering voice hearers were conducted and analysed using interpretative phenomenological analysis (IPA). Discussion: The self-help group gives significant help in identification and dealing with the voices; therefore, it serves as turning point in the life story of voice hearers. Conclusion: Applying self-help group in clinical context contributes to better outcomes in treatment of voice hearers.

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Jaleel, Hanan Qassim, Jane Jaleel Stephan i Sinan A. Naji. "Gender Identification from Speech Recognition Using Machine Learning Techniques and Convolutional Neural Networks". Webology 19, nr 1 (20.01.2022): 1666–88. http://dx.doi.org/10.14704/web/v19i1/web19112.

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Gender identification represents a fundamental component of speech recognition and automatic interacting sound responding systems. Identifying the voice gender minimizes the computational loads of these systems for additional processing. Standard approaches for gender estimation from the speech have broadly relied on the extraction of speech features and classification tasks. This paper proposes a technique for gender identification of speech samples using the speech recognition process. The proposed technique extracts essential voice features like Mean, Zero-Crossing, Standard Deviation, and Amplitude, as well as 12 most significant features from every voice sample, and combines them to create voice feature vectors. The proposed technique uses several machine and deep learning methods such as Random Forest, KNN, Logistic Regression, Decision Tree, and CNNs, in order to classify the voice vectors into Male and Female classes. After comparing the evaluation metrics results of all classifiers, the proposed technique finds out that the CNN model is the best classifier used to classify the voice vectors with a higher precision value of 1.0.

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Ramadhina, Dea Sifana, Rita Magdalena i Sofia Saidah. "Individual Identification Through Voice Using Mel-Frequency Cepstrum Coefficient (MFCC) and Hidden Markov Models (HMM) Method". Journal of Measurements, Electronics, Communications, and Systems 7, nr 1 (30.12.2020): 26. http://dx.doi.org/10.25124/jmecs.v7i1.3553.

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Voice is one of the parameters in the identification process of a person. Through the voice, information will be obtained such as gender, age, and even the identity of the speaker. Speaker recognition is a method to narrow down crimes and frauds committed by voice. So that it will minimize the occurrence of faking one's identity. The Method of Mel Frequency Cepstrum Coefficient (MFCC) can be used in the speech recognition system. The process of feature extraction of speech signal using MFCC will produce acoustic speech signal. The classification, Hidden Markov Models (HMM) is used to match unidentified speaker’s voice with the voices in database. In this research, the system is used to verify the speaker, namely 15 text dependent in Indonesian. On testing the speaker with the same as database, the highest accuracy is 99,16%.

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Zhou, Yinghui, Yali Liu i Huan Niu. "Perceptual Characteristics of Voice Identification in Noisy Environments". Applied Sciences 12, nr 23 (27.11.2022): 12129. http://dx.doi.org/10.3390/app122312129.

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Auditory analysis is an essential method that is used to recognize voice identity in court investigations. However, noise will interfere with auditory perception. Based on this, we selected white noise, pink noise, and speech noise in order to design and conduct voice identity perception experiments. Meanwhile, we explored the impact of the noise type and frequency distribution on voice identity perception. The experimental results show the following: (1) in high signal-to-noise ratio (SNR) environments, there is no significant difference in the impact of noise types on voice identity perception; (2) in low SNR environments, the perceived result of speech noise is significantly different from that of white noise and pink noise, and the interference is more obvious; (3) in the speech noise with a low SNR (−8 dB), the voice information contained in the high-frequency band of 2930~6250 Hz is helpful for achieving accuracy in voice identity perception. These results show that voice identity perception in a better voice transmission environment is mainly based on the acoustic information provided by the low-frequency and medium-frequency bands, which concentrate most of the energy of the voice. As the SNR gradually decreases, a human’s auditory mechanism will automatically expand the receiving frequency range to obtain more effective acoustic information from the high-frequency band. Consequently, the high-frequency information ignored in the objective algorithm may be more robust with respect to identity perception in our environment. The experimental studies not only evaluate the quality of the case voice and control the voice recording environment, but also predict the accuracy of voice identity perception under noise interference. This research provides the theoretical basis and data support for applying voice identity perception in forensic science.

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Amalia, Sitti. "Identification Of Number Using Artificial Neural Network Backpropagation". MATEC Web of Conferences 215 (2018): 01011. http://dx.doi.org/10.1051/matecconf/201821501011.

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This research proposed to design and implementation system of voice pattern recognition in the form of numbers with offline pronunciation. Artificial intelligent with backpropagation algorithm used on the simulation test. The test has been done to 100 voice files which got from 10 person voices for 10 different numbers. The words are consisting of number 0 to 9. The trial has been done with artificial neural network parameters such as tolerance value and the sum of a neuron. The best result is shown at tolerance value varied and a sum of the neuron is fixed. The percentage of this network training with optimal architecture and network parameter for each training data and new data are 82,2% and 53,3%. Therefore if tolerance value is fixed and a sum of neuron varied gave 82,2% for training data and 54,4% for new data

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Rachman, Laura, Almut Jebens i Deniz Baskent. "Phonological but not lexical processing alters the perceptual weighting of mean fundamental frequency and vocal-tract length cues for voice gender categorisation". Journal of the Acoustical Society of America 151, nr 4 (kwiecień 2022): A262. http://dx.doi.org/10.1121/10.0011271.

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Listeners use various voice cues to segregate different speakers, or to infer speaker-related information such as perceived gender. Two important anatomically related voices cues used for speaker identification, including perceived gender, are mean fundamental frequency (F0), related to the glottal pulse rate, and vocal-tract length (VTL), correlating with body size. Voice cue processing seems to be affected by linguistic processes, such that voice perception is more precise when listeners hear speakers in their native language compared to a non-native language. In addition, recent research shows that F0 and VTL sensitivity is lower for words compared to time-reversed words, either because time-reversed words are unintelligible or phonemes are distorted in voice-onset times and aspirations, pointing to effects of lexical or phonological processing. However, voice cue sensitivity and using these cues to infer speaker-related information may rely on different mechanisms. Here, we studied effects of lexical and phonological processing on F0 and VTL cue weighting for one aspect of speaker identification, namely voice gender categorisation, by manipulating these cues in three linguistic conditions: meaningful words; phonotactically plausible nonwords; and phonotactically implausible time-reversed nonwords. We found that F0 and VTL weighting for voice gender categorisation was affected by phonological but not by lexical processing.

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Lutsenko, K., i K. Nikulin. "VOICE SPEAKER IDENTIFICATION AS ONE OF THE CURRENT BIOMETRIC METHODS OF IDENTIFICATION OF A PERSON". Theory and Practice of Forensic Science and Criminalistics 19, nr 1 (2.04.2020): 239–55. http://dx.doi.org/10.32353/khrife.1.2019.18.

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The article deals with the most widespread biometric identification systems of individuals, including voice recognition of the speaker on video and sound recordings. The urgency of the topic of identification of a person is due to the active informatization of modern society and the increase of flows of confidential information. The branches of the use of biometric technologies and their general characteristics are given. Here is an overview of the use of identification groups that characterize the voice. Also in the article the division of voice identification systems into the corresponding classes is given. The main advantages of voice biometrics such as simplicity of system realization are considered; low cost (the lowest among all biometric methods); No need for contact, the voice biometry allows for long-range verification, unlike other biometric technologies. The analysis of existing methods of speech recognition recognition identifying a person by a combination of unique voice characteristics, determining their weak and strong points, on the basis of which the choice of the most appropriate method for solving the problem of text-independent recognition, Namely the model of Gaussian mixtures, was carried out. The prerequisite for the development of speech technologies is a significant increase in computing capabilities, memory capacity with a significant reduction in the size of computer systems. It should also be Noted the development of mathematical methods that make it possible to perform the Necessary processing of an audio signal by isolating informative features from it. It has been established that the development of information technologies, and the set of practical applications, which use voice recognition technologies, make this area relevant for further theoretical and practical research.

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Stockmal, Verna, i Z. S. Bond. "On talker voice in language identification". Journal of the Acoustical Society of America 110, nr 5 (listopad 2001): 2684. http://dx.doi.org/10.1121/1.4777204.

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Shipp, Thomas, E. Thomas Doherty i Harry Hollien. "Some fundamental considerations regarding voice identification". Journal of the Acoustical Society of America 82, nr 2 (sierpień 1987): 687–88. http://dx.doi.org/10.1121/1.395418.

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Koenig, Bruce E. "Spectrographic voice identification: A forensic survey". Journal of the Acoustical Society of America 79, nr 6 (czerwiec 1986): 2088–90. http://dx.doi.org/10.1121/1.393170.

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Skuk, Verena G., i Stefan R. Schweinberger. "Gender differences in familiar voice identification". Hearing Research 296 (luty 2013): 131–40. http://dx.doi.org/10.1016/j.heares.2012.11.004.

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Thompson, Charles P. "A language effect in voice identification". Applied Cognitive Psychology 1, nr 2 (kwiecień 1987): 121–31. http://dx.doi.org/10.1002/acp.2350010205.

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Li, Jie, Qiaozhuan Liang, Zhenzhen Zhang i Xiao Wang. "Leader humility and constructive voice behavior in China: a dual process model". International Journal of Manpower 39, nr 6 (3.09.2018): 840–54. http://dx.doi.org/10.1108/ijm-06-2017-0137.

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PurposeThe purpose of this paper is to find how leader humility affects employees’ constructive voice behavior toward supervisor (speaking up) and coworkers (speaking out) from an identification-based perspective, and seeks to verify the effectiveness of leader humility in the Chinese context.Design/methodology/approachData were collected from 325 employees in four Chinese companies with two phases. In the first phase, the participants were asked to report the leader humility, their identification of their relations with the supervisor, and their identification with their organization. In the second phase, they were asked to report their voice behaviors toward their supervisors and coworkers.FindingsThe results indicate that leader humility strongly predicts both employees’ voice behaviors of speaking up and speaking out. Results further suggest that relational identification with the supervisor explains why leader humility promotes employees speaking up, while organizational identification explains why leader humility promotes employees speaking up and speaking out.Practical implicationsManagers with humility can successfully shape employees’ relational and organizational identifications, which in turn encourage their voice behaviors toward supervisors and coworkers. Hence, behaving humbly in working places could be an effective way for managers to promote organizational cohesion and creativity.Originality/valueAlthough leader humility attracts much attention in both academia and practice, researchers have been primarily focusing on conceptual development and measurement issues, and empirical studies are rare. This is the first research connecting leader humility and employee proactive behaviors. Moreover, it takes an in-depth analysis of the constructive voice behaviors by differentiating them based on their targets.

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Choi, Ji Hyun, Dong Uk Cho i Yeon Man Jeong. "Identification of Voice for Listeners who Feel Favor Using Voice Analysis". Journal of Korean Institute of Communications and Information Sciences 41, nr 1 (31.01.2016): 122–31. http://dx.doi.org/10.7840/kics.2015.41.1.122.

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Cheng, Andrew, i Steve Cho. "The Effect of Ethnicity on Identification of Korean American Speech". Languages 6, nr 4 (9.11.2021): 186. http://dx.doi.org/10.3390/languages6040186.

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Research on ethnic varieties of American English has found that listeners can identify speaker ethnicity from voice alone at above-chance rates. This study aims to extend this research by focusing on the perception of race and ethnicity in the voices of ethnically Korean speakers of English. Bilingual Korean Americans in California provided samples of English speech that were rated by 105 listeners. Listeners rated the speakers on their likelihood of being a certain race or ethnicity (including Asian and White). Listeners who were Korean themselves rated the speakers as more likely to be Asian and Korean, whereas non-Asian listeners rated the speakers as more likely to be White. Non-Asian listeners also demonstrated a negative correlation between rating a voice as Asian and rating a voice as belonging to a native-born American, while Asian listeners did not. Finally, a positive correlation between pitch and perceived Asianness was found for female speakers, corresponding to listeners’ metalinguistic commentary about the hallmarks and stereotypes of Asian or Asian American speech. The findings implicate the listener’s own ethnic identity and familiarity with a speech variety as an important factor in sociolinguistic perception.

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Lin, Honghuang, Cody Karjadi, Ting F. A. Ang, Joshi Prajakta, Chelsea McManus, Tuka W. Alhanai, James Glass i Rhoda Au. "Identification of digital voice biomarkers for cognitive health". Exploration of Medicine 1, nr 6 (31.12.2020): 406–17. http://dx.doi.org/10.37349/emed.2020.00028.

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Aim: Human voice contains rich information. Few longitudinal studies have been conducted to investigate the potential of voice to monitor cognitive health. The objective of this study is to identify voice biomarkers that are predictive of future dementia. Methods: Participants were recruited from the Framingham Heart Study. The vocal responses to neuropsychological tests were recorded, which were then diarized to identify participant voice segments. Acoustic features were extracted with the OpenSMILE toolkit (v2.1). The association of each acoustic feature with incident dementia was assessed by Cox proportional hazards models. Results: Our study included 6, 528 voice recordings from 4, 849 participants (mean age 63 ± 15 years old, 54.6% women). The majority of participants (71.2%) had one voice recording, 23.9% had two voice recordings, and the remaining participants (4.9%) had three or more voice recordings. Although all asymptomatic at the time of examination, participants who developed dementia tended to have shorter segments than those who were dementia free (P< 0.001). Additionally, 14 acoustic features were significantly associated with dementia after adjusting for multiple testing (P< 0.05/48 = 1 × 10–3). The most significant acoustic feature was jitterDDP_sma_de (P = 7.9 × 10–7), which represents the differential frame-to-frame Jitter. A voice based linear classifier was also built that was capable of predicting incident dementia with area under curve of 0.812. Conclusions: Multiple acoustic and linguistic features are identified that are associated with incident dementia among asymptomatic participants, which could be used to build better prediction models for passive cognitive health monitoring.

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Zakariah, Mohammed, Reshma B, Yousef Ajmi Alothaibi, Yanhui Guo, Kiet Tran-Trung i Mohammad Mamun Elahi. "An Analytical Study of Speech Pathology Detection Based on MFCC and Deep Neural Networks". Computational and Mathematical Methods in Medicine 2022 (4.04.2022): 1–15. http://dx.doi.org/10.1155/2022/7814952.

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Diseases of internal organs other than the vocal folds can also affect a person’s voice. As a result, voice problems are on the rise, even though they are frequently overlooked. According to a recent study, voice pathology detection systems can successfully help the assessment of voice abnormalities and enable the early diagnosis of voice pathology. For instance, in the early identification and diagnosis of voice problems, the automatic system for distinguishing healthy and diseased voices has gotten much attention. As a result, artificial intelligence-assisted voice analysis brings up new possibilities in healthcare. The work was aimed at assessing the utility of several automatic speech signal analysis methods for diagnosing voice disorders and suggesting a strategy for classifying healthy and diseased voices. The proposed framework integrates the efficacy of three voice characteristics: chroma, mel spectrogram, and mel frequency cepstral coefficient (MFCC). We also designed a deep neural network (DNN) capable of learning from the retrieved data and producing a highly accurate voice-based disease prediction model. The study describes a series of studies using the Saarbruecken Voice Database (SVD) to detect abnormal voices. The model was developed and tested using the vowels /a/, /i/, and /u/ pronounced in high, low, and average pitches. We also maintained the “continuous sentence” audio files collected from SVD to select how well the developed model generalizes to completely new data. The highest accuracy achieved was 77.49%, superior to prior attempts in the same domain. Additionally, the model attains an accuracy of 88.01% by integrating speaker gender information. The designed model trained on selected diseases can also obtain a maximum accuracy of 96.77% ( cordectomy × healthy ). As a result, the suggested framework is the best fit for the healthcare industry.

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Mamyrbayev, Orken, Aizat Kydyrbekova, Keylan Alimhan, Dina Oralbekova, Bagashar Zhumazhanov i Bulbul Nuranbayeva. "Development of security systems using DNN and i & x-vector classifiers". Eastern-European Journal of Enterprise Technologies 4, nr 9(112) (31.08.2021): 32–45. http://dx.doi.org/10.15587/1729-4061.2021.239186.

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The widespread use of biometric systems entails increased interest from cybercriminals aimed at developing attacks to crack them. Thus, the development of biometric identification systems must be carried out taking into account protection against these attacks. The development of new methods and algorithms for identification based on the presentation of randomly generated key features from the biometric base of user standards will help to minimize the disadvantages of the above methods of biometric identification of users. We present an implementation of a security system based on voice identification as an access control key and a verification algorithm developed using MATLAB function blocks that can authenticate a person's identity by his or her voice. Our research has shown an accuracy of 90 % for this user identification system for individual voice characteristics. It has been experimentally proven that traditional MFCCs using DNN and i and x-vector classifiers can achieve good results. The paper considers and analyzes the most well-known approaches from the literature to the problem of user identification by voice: dynamic programming methods, vector quantization, mixtures of Gaussian processes, hidden Markov model. The developed software package for biometric identification of users by voice and the method of forming the user's voice standards implemented in the complex allows reducing the number of errors in identifying users of information systems by voice by an average of 1.5 times. Our proposed system better defines voice recognition in terms of accuracy, security and complexity. The application of the results obtained will improve the security of the identification process in information systems from various attacks.

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Karlsson, Thilda, Heidi Schaefer, Jason J. S. Barton i Sherryse L. Corrow. "Effects of Voice and Biographic Data on Face Encoding". Brain Sciences 13, nr 1 (14.01.2023): 148. http://dx.doi.org/10.3390/brainsci13010148.

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There are various perceptual and informational cues for recognizing people. How these interact in the recognition process is of interest. Our goal was to determine if the encoding of faces was enhanced by the concurrent presence of a voice, biographic data, or both. Using a between-subject design, four groups of 10 subjects learned the identities of 24 faces seen in video-clips. Half of the faces were seen only with their names, while the other half had additional information. For the first group this was the person’s voice, for the second, it was biographic data, and for the third, both voice and biographic data. In a fourth control group, the additional information was the voice of a generic narrator relating non-biographic information. In the retrieval phase, subjects performed a familiarity task and then a face-to-name identification task with dynamic faces alone. Our results consistently showed no benefit to face encoding with additional information, for either the familiarity or identification task. Tests for equivalency indicated that facilitative effects of a voice or biographic data on face encoding were not likely to exceed 3% in accuracy. We conclude that face encoding is minimally influenced by cross-modal information from voices or biographic data.

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Ruan, Rongbin, i Wan Chen. "Relationship between organizational identification and employee voice: A meta-analysis". Social Behavior and Personality: an international journal 49, nr 10 (6.10.2021): 1–12. http://dx.doi.org/10.2224/sbp.10316.

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The extant literature contains conflicting findings about the relationship between organizational identification and employee voice. To estimate a more realistic correlation between these two variables, we conducted a meta-analysis of 40 empirical studies associated with organizational identification and employee voice. We also analyzed cultural context, education level, common method variance, and the measurement scale used in each study as moderators of the relationship between organizational identification and employee voice. The results show that organizational identification had a positive association with employee voice, and that the moderating role of cultural context was not significant, whereas education level, measurement scales, and common method variance were significant moderators. On the basis of our meta-analysis results, we propose that human resource managers pay attention to the effect of organizational identification in eliciting employee voice, and implement policies that allow employees to express more ideas that promote organizational development in practice.

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Sheldon, Deborah A. "Effects of Multiple Listenings on Error-Detection Acuity in Multivoice, Multitimbral Musical Examples". Journal of Research in Music Education 52, nr 2 (lipiec 2004): 102–15. http://dx.doi.org/10.2307/3345433.

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This study is an investigation of the effects of multiple listenings on error-detection identification and labeling accuracy among brass and woodwind instrumentalists. Examples derived from band music used balanced four-voice incipits performed with differing timbres, and errors that occurred in one or multiple voices. Response rates for correct and incorrect identification and labeling of errors were greatest during the first listening, less for the second, and least for the third. Identification mistakes outnumbered correct responses in the last two listenings. Error-labeling mistakes outnumbered correct responses in all listenings. Most correct responses in the first listening were made in the top two lines. During all listenings, the fewest correct identification and labeling responses occurred in the bottommost voice. Data suggest that multiple listenings and a correct aural referent may not help error-detection acuity in multitimbral, multivoice settings. These data may be useful in informing practice in conducting and methods instruction during teacher-preparation programs.

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Zhuang, Wen-Long, Chun-Han Lee i Chung-Liang Ma. "Hotel Employees’ Voice Behavior: The Role of Paternalistic Leadership and Organizational Identification". Sustainability 14, nr 1 (27.12.2021): 256. http://dx.doi.org/10.3390/su14010256.

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This study explores the effect of paternalistic leadership (moral leadership, benevolent leadership, and authoritarian leadership) on hotel employees’ voice behavior and the moderating role of organizational identification. This study samples employees of five-star hotels in northern, central, and southern Taiwan. Purposive sampling is used to distribute 450 questionnaires: 150 in northern Taiwan, 150 in central Taiwan, and 150 in southern Taiwan. The number of valid questionnaires was 359, and the effective questionnaire recovery rate was 79.78%. The analysis results indicate that (1) supervisors’ moral leadership negatively affects hotel employees’ voice behavior, (2) supervisors’ benevolent leadership positively affects hotel employees’ voice behavior, (3) supervisors’ authoritarian leadership negatively affects hotel employees’ voice behavior, (4) organizational identification moderates the relationship between moral leadership and voice behavior, (5) organizational identification moderates the relationship between benevolent leadership and voice behavior, and (6) organizational identification moderates the relationship between authoritarian leadership and voice behavior. This study also proposes managerial implications based on the analysis results. This research attempts to make contributions to the literatures of hospitality and tourism.

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Soloviev, Viktor, Oleg Rybalsky, Vadim Zhuravel, Alexander Shablya i Evgeny Timko. "TAKING INTO ACCOUNT THE MULTIFACTORIAL CHARACTER OF VOICE CHARACTERISTICS IN THE PROBLEMS OF SPEAKER IDENTIFICATION". Journal of Automation and Information sciences 5 (1.09.2021): 21–30. http://dx.doi.org/10.34229/1028-0979-2021-5-2.

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When testing the most advanced speaker identification systems on specialized databases, their minimum efficiency, estimated by the error probability at the point of intersection of the error curves, is only a few percent. However, many factors are known that affect the variability of the characteristics of the speaker's voice, each of which has its own, different from the others, influence on the results of the speaker's identification by the characteristics of the voice. The complexity of creating and testing speaker identification systems is the need to quantitatively formalize a number of specific factors that affect the characteristics of his voice. The article discusses the proposed method for accounting for a variety of factors affecting the parameters of the characteristics of the speaker's voice, which provides the fundamental possibility of indirectly accounting for their practically unlimited number. According to this method, «atomic» structures are distinguished from speech signals, which depend on the totality of the main factors that affect the speaker's identification process. With this method, all significant factors affecting the characteristics of the voice will be indirectly taken into account at the level of these structures. Subsequent decisions are made on the combinatorial set of a huge number of these «atomic» structures. «Atomic» speech structures are understood as the spectra of any fragments of any vowel sounds allocated in a time window of 20 ms. «Atomic» structures are selected automatically. The proposed method provides a rational consideration of the multifactorial influence of various parameters, since the spectra of these structures are influenced by all the main factors that characterize the individuality of the voice of a particular speaker. The decision on the identity of the voices of the announcers recorded on different phonograms is carried out on the basis of combinatorics of «atomic» spectra of vowel sounds in both phonograms. The method has shown high efficiency in the examination of phonograms of short duration.

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Breznik, Jelka. "The impact of voice on speech realization". Journal of Education Culture and Society 5, nr 2 (6.01.2020): 93–101. http://dx.doi.org/10.15503/jecs20142.93.101.

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The study discusses spoken literary language and the impact of voice on speech realization. The voice consists of a sound made by a human being using the vocal folds for talking, singing, laughing, crying, screaming… The human voice is specifically the part of human sound production in which the vocal folds (vocal cords) are the primary sound source. Our voice is our instrument and identity card. How does the voice (voice tone) affect others and how do they respond, positively or negatively? How important is voice (voice tone) in communication process? The study presents how certain individuals perceive voice. The results of the research on the relationships between the spoken word, excellent speaker, voice and description / definition / identification of specific voices done by experts in the field of speech and voice as well as non-professionals are presented. The study encompasses two focus groups. One consists of amateurs (non-specialists in the field of speech or voice who have no knowledge in this field) and the other consists of professionals who work with speech or language or voice. The questions were intensified from general to specific, directly related to the topic. The purpose of such a method of questioning was to create relaxed atmosphere, promote discussion, allow participants to interact, complement, and to set up self-listening and additional comments.

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Alkhatib, Bassel, i Mohammad Madian Waleed Kamal Eddin. "Voice Identification Using MFCC and Vector Quantization". Baghdad Science Journal 17, nr 3(Suppl.) (8.09.2020): 1019. http://dx.doi.org/10.21123/bsj.2020.17.3(suppl.).1019.

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The speaker identification is one of the fundamental problems in speech processing and voice modeling. The speaker identification applications include authentication in critical security systems and the accuracy of the selection. Large-scale voice recognition applications are a major challenge. Quick search in the speaker database requires fast, modern techniques and relies on artificial intelligence to achieve the desired results from the system. Many efforts are made to achieve this through the establishment of variable-based systems and the development of new methodologies for speaker identification. Speaker identification is the process of recognizing who is speaking using the characteristics extracted from the speech's waves like pitch, tone, and frequency. The speaker's models are created and saved in the system environment and used to verify the identity required by people accessing the systems, which allows access to various services that are controlled by voice, speaker identification involves two main parts: the first part is the feature extraction and the second part is the feature matching.

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