Relevant bibliographies by topics / Voice recognition

Academic literature on the topic 'Voice recognition'

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Published: 4 June 2021
Last updated: 20 February 2023

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Journal articles on the topic "Voice recognition"

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Sangani, K. "Inner voice [voice recognition]." Engineering & Technology 8, no. 7 (August 1, 2013): 36–37. http://dx.doi.org/10.1049/et.2013.0717.

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Mehta, Amit, and Theresa C. McLoud. "Voice Recognition." Journal of Thoracic Imaging 18, no. 3 (July 2003): 178–82. http://dx.doi.org/10.1097/00005382-200307000-00007.

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Schreiber, Melvyn H. "Voice recognition." Academic Radiology 5, no. 12 (December 1998): 871–72. http://dx.doi.org/10.1016/s1076-6332(98)80251-1.

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Schweinberger, Stefan R., Anja Herholz, and Volker Stief. "Auditory Long term Memory: Repetition Priming of Voice Recognition." Quarterly Journal of Experimental Psychology Section A 50, no. 3 (August 1997): 498–517. http://dx.doi.org/10.1080/713755724.

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Two experiments examined repetition priming in the recognition of famous voices. In Experiment 1, reaction times for fame decisions to famous voice samples were shorter than in an unprimed condition, when voices were primed by a different voice sample of the same person having been presented in an earlier phase of the experiment. No effect of voice repetition was observed for non-famous voices. In Experiment 2, it was investigated whether this priming effect is voice-specific or whether it is related to post-perceptual processes in person recognition. Recognizing a famous voice was again primed by having earlier heard a different voice sample of that person. Although an earlier exposure to that person's name did not cause any priming, there was some indication of priming following an earlier exposure to that person's face. Finally, earlier exposure to the identical voice sample (as compared to a different voice sample from the same person) caused a considerable bias towards responding “famous”—i.e. performance benefits for famous but costs for nonfamous voices. The findings suggest that (1) repetition priming invoice recognition primarily involves the activation of perceptual representations of voices, and (2) it is important to determine the conditions in which priming causes bias effects that need to be disentangled from performance benefits.
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Doyle, Sean. "Determining voice recognition accuracy in a voice recognition system." Journal of the Acoustical Society of America 128, no. 2 (2010): 964. http://dx.doi.org/10.1121/1.3481753.

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Plante-Hébert, Julien, Victor J. Boucher, and Boutheina Jemel. "The processing of intimately familiar and unfamiliar voices: Specific neural responses of speaker recognition and identification." PLOS ONE 16, no. 4 (April 16, 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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Haag, Andreas. "VOICE CONTROL WITH SEMANTIC VOICE RECOGNITION." ATZelektronik worldwide 7, no. 6 (October 2012): 50–53. http://dx.doi.org/10.1365/s38314-012-0136-8.

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Searcy, Gus, and Franz Kavan. "Voice recognition system." Journal of the Acoustical Society of America 93, no. 6 (June 1993): 3541–42. http://dx.doi.org/10.1121/1.405347.

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Searcy, Gus. "Voice recognition system." Journal of the Acoustical Society of America 94, no. 2 (August 1993): 1181. http://dx.doi.org/10.1121/1.406911.

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Muroi, Tetsuya. "Voice recognition system." Journal of the Acoustical Society of America 90, no. 3 (September 1991): 1711. http://dx.doi.org/10.1121/1.401729.

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Dissertations / Theses on the topic "Voice recognition"

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Kamarauskas, Juozas. "Speaker recognition by voice." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2009. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2009~D_20090615_093847-20773.

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Questions of speaker’s recognition by voice are investigated in this dissertation. Speaker recognition systems, their evolution, problems of recognition, systems of features, questions of speaker modeling and matching used in text-independent and text-dependent speaker recognition are considered too. The text-independent speaker recognition system has been developed during this work. The Gaussian mixture model approach was used for speaker modeling and pattern matching. The automatic method for voice activity detection was proposed. This method is fast and does not require any additional actions from the user, such as indicating patterns of the speech signal and noise. The system of the features was proposed. This system consists of parameters of excitation source (glottal) and parameters of the vocal tract. The fundamental frequency was taken as an excitation source parameter and four formants with three antiformants were taken as parameters of the vocal tract. In order to equate dispersions of the formants and antiformants we propose to use them in mel-frequency scale. The standard mel-frequency cepstral coefficients (MFCC) for comparison of the results were implemented in the recognition system too. These features make baseline in speech and speaker recognition. The experiments of speaker recognition have shown that our proposed system of features outperformed standard mel-frequency cepstral coefficients. The equal error rate (EER) was equal to 5.17% using proposed... [to full text]
Disertacijoje nagrinėjami kalbančiojo atpažinimo pagal balsą klausimai. Aptartos kalbančiojo atpažinimo sistemos, jų raida, atpažinimo problemos, požymių sistemos įvairovė bei kalbančiojo modeliavimo ir požymių palyginimo metodai, naudojami nuo ištarto teksto nepriklausomame bei priklausomame kalbančiojo atpažinime. Darbo metu sukurta nuo ištarto teksto nepriklausanti kalbančiojo atpažinimo sistema. Kalbėtojų modelių kūrimui ir požymių palyginimui buvo panaudoti Gauso mišinių modeliai. Pasiūlytas automatinis vokalizuotų garsų išrinkimo (segmentavimo) metodas. Šis metodas yra greitai veikiantis ir nereikalaujantis iš vartotojo jokių papildomų veiksmų, tokių kaip kalbos signalo ir triukšmo pavyzdžių nurodymas. Pasiūlyta požymių vektorių sistema, susidedanti iš žadinimo signalo bei balso trakto parametrų. Kaip žadinimo signalo parametras, panaudotas žadinimo signalo pagrindinis dažnis, kaip balso trakto parametrai, panaudotos keturios formantės bei trys antiformantės. Siekiant suvienodinti žemesnių bei aukštesnių formančių ir antiformančių dispersijas, jas pasiūlėme skaičiuoti melų skalėje. Rezultatų palyginimui sistemoje buvo realizuoti standartiniai požymiai, naudojami kalbos bei asmens atpažinime – melų skalės kepstro koeficientai (MSKK). Atlikti kalbančiojo atpažinimo eksperimentai parodė, kad panaudojus pasiūlytą požymių sistemą buvo gauti geresni atpažinimo rezultatai, nei panaudojus standartinius požymius (MSKK). Gautas lygių klaidų lygis, panaudojant pasiūlytą požymių... [toliau žr. visą tekstą]
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Al-Kilani, Menia. "Voice-signature-based Speaker Recognition." University of the Western Cape, 2017. http://hdl.handle.net/11394/5888.

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Magister Scientiae - MSc (Computer Science)
Personal identification and the protection of data are important issues because of the ubiquitousness of computing and these have thus become interesting areas of research in the field of computer science. Previously people have used a variety of ways to identify an individual and protect themselves, their property and their information. This they did mostly by means of locks, passwords, smartcards and biometrics. Verifying individuals by using their physical or behavioural features is more secure than using other data such as passwords or smartcards, because everyone has unique features which distinguish him or her from others. Furthermore the biometrics of a person are difficult to imitate or steal. Biometric technologies represent a significant component of a comprehensive digital identity solution and play an important role in security. The technologies that support identification and authentication of individuals is based on either their physiological or their behavioural characteristics. Live-­‐data, in this instance the human voice, is the topic of this research. The aim is to recognize a person’s voice and to identify the user by verifying that his/her voice is the same as a record of his / her voice-­‐signature in a systems database. To address the main research question: “What is the best way to identify a person by his / her voice signature?”, design science research, was employed. This methodology is used to develop an artefact for solving a problem. Initially a pilot study was conducted using visual representation of voice signatures, to check if it is possible to identify speakers without using feature extraction or matching methods. Subsequently, experiments were conducted with 6300 data sets derived from Texas Instruments and the Massachusetts Institute of Technology audio database. Two methods of feature extraction and classification were considered—mel frequency cepstrum coefficient and linear prediction cepstral coefficient feature extraction—and for classification, the Support Vector Machines method was used. The three methods were compared in terms of their effectiveness and it was found that the system using the mel frequency cepstrum coefficient, for feature extraction, gave the marginally better results for speaker recognition.
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Alkilani, Menia Mohamed. "Voice signature based Speaker Recognition." University of the Western Cape, 2017. http://hdl.handle.net/11394/6196.

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Magister Scientiae - MSc (Computer Science)
Personal identification and the protection of data are important issues because of the ubiquitousness of computing and these havethus become interesting areas of research in the field of computer science. Previously people have used a variety of ways to identify an individual and protect themselves, their property and their information.
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Laird, Esther. "Voice recognition and auditory-visual integration in person recognition." Thesis, University of Sussex, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.487906.

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The human ability to recognise a voice is important for social interaction and speech comprehension. In everyday recognitions, the voice can be encountered alone (e.g. over a telephone) or with a face, and ~e person being recognised can be familiar or unfamiliar (such as a witness choosing a perpetrator from a lineup). This thesis - presents 7 studies cov~ring each of these situations. The first paper presents 3 studies on recognition of unfamiliar voices when there is a change in emotional tone between learning and test phases. A tone change reduces recognition accuracy when there is no specific encoding strategy at the learning phase. Familiaris.ation at the learning phase reduces the tone change effect but concentrating on word content at the learning phase does not. The second paper presents 3 studies investigating the limitations of the face overshadowing effect (voice recognition is worse when the voice is learned with a face than if it is learned alone). Blurring faces made face recognition more qifficult but did not affect voice recognition. In experiment 2, participants learned a sentence repeated 3 times, either with the face changing on each repetition or staying the same. Face recognition accuracy was lower when there were 3 faces, but this did not affect voice recognition. In experiment 3, inverting faces' made face recognition more difficult but did not affect voice recognition. The third paper reports that episodic memory for a celebrity is improved when a face and voice are given compared to just a face. A model of person recognition is presented that builds on existing models (e.g. Burton, Bruce & Johnston, 1990; Belin, 2004). It accounts for unfamiliar and familiar voice recognition and the benefits and costs of auditory-visual integration.
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Clotworthy, Christopher John. "A study of automated voice recognition." Thesis, Queen's University Belfast, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.356909.

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Damjanovic, Ljubica. "Memory processes in familiar voice recognition." Thesis, University of Essex, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.413126.

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Vipperla, Ravichander. "Automatic Speech Recognition for ageing voices." Thesis, University of Edinburgh, 2011. http://hdl.handle.net/1842/5725.

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With ageing, human voices undergo several changes which are typically characterised by increased hoarseness, breathiness, changes in articulatory patterns and slower speaking rate. The focus of this thesis is to understand the impact of ageing on Automatic Speech Recognition (ASR) performance and improve the ASR accuracies for older voices. Baseline results on three corpora indicate that the word error rates (WER) for older adults are significantly higher than those of younger adults and the decrease in accuracies is higher for males speakers as compared to females. Acoustic parameters such as jitter and shimmer that measure glottal source disfluencies were found to be significantly higher for older adults. However, the hypothesis that these changes explain the differences in WER for the two age groups is proven incorrect. Experiments with artificial introduction of glottal source disfluencies in speech from younger adults do not display a significant impact on WERs. Changes in fundamental frequency observed quite often in older voices has a marginal impact on ASR accuracies. Analysis of phoneme errors between younger and older speakers shows a pattern of certain phonemes especially lower vowels getting more affected with ageing. These changes however are seen to vary across speakers. Another factor that is strongly associated with ageing voices is a decrease in the rate of speech. Experiments to analyse the impact of slower speaking rate on ASR accuracies indicate that the insertion errors increase while decoding slower speech with models trained on relatively faster speech. We then propose a way to characterise speakers in acoustic space based on speaker adaptation transforms and observe that speakers (especially males) can be segregated with reasonable accuracies based on age. Inspired by this, we look at supervised hierarchical acoustic models based on gender and age. Significant improvements in word accuracies are achieved over the baseline results with such models. The idea is then extended to construct unsupervised hierarchical models which also outperform the baseline models by a good margin. Finally, we hypothesize that the ASR accuracies can be improved by augmenting the adaptation data with speech from acoustically closest speakers. A strategy to select the augmentation speakers is proposed. Experimental results on two corpora indicate that the hypothesis holds true only when the amount of available adaptation is limited to a few seconds. The efficacy of such a speaker selection strategy is analysed for both younger and older adults.
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Iliadi, Konstantina. "Bio-inspired voice recognition for speaker identification." Thesis, University of Southampton, 2016. https://eprints.soton.ac.uk/413949/.

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Speaker identification (SID) aims to identify the underlying speaker(s) given a speech utterance. In a speaker identification system, the first component is the front-end or feature extractor. Feature extraction transforms the raw speech signal into a compact but effective representation that is more stable and discriminative than the original signal. Since the front-end is the first component in the chain, the quality of the later components is strongly determined by its quality. Existing approaches have used several feature extraction methods that have been adopted directly from the speech recognition task. However, the nature of these two tasks is contradictory given that speaker variability is one of the major error sources in speech recognition whereas in speaker recognition, it is the information that we wish to extract. In this thesis, the possible benefits of adapting a biologically-inspired model of human auditory processing as part of the front-end of a SID system are examined. This auditory model named Auditory Image Model (AIM) generates the stabilized auditory image (SAI). Features are extracted by the SAI through breaking it into boxes of different scales. Vector quantization (VQ) is used to create the speaker database with the speakers’ reference templates that will be used for pattern matching with the features of the target speakers that need to be identified. Also, these features are compared to the Mel-frequency cepstral coefficients (MFCCs), which is the most evident example of a feature set that is extensively used in speaker recognition but originally developed for speech recognition purposes. Additionally, another important parameter in SID systems is the dimensionality of the features. This study addresses this issue by specifying the most speaker-specific features and trying to further improve the system configuration for obtaining a representation of the auditory features with lower dimensionality. Furthermore, after evaluating the system performance in quiet conditions, another primary topic of speaker recognition is investigated. SID systems can perform well under matched training and test conditions but their performance degrades significantly because of the mismatch caused by background noise in real-world environments. Achieving robustness to SID systems becomes an important research problem. In the second experimental part of this thesis, the developed version of the system is assessed for speaker data sets of different size. Clean speech is used for the training phase while speech in the presence of babble noise is used for speaker testing. The results suggest that the extracted auditory feature vectors lead to much better performance, i.e. higher SID accuracy, compared to the MFCC-based recognition system especially for low SNRs. Lastly, the system performance is inspected with regard to parameters related to the training and test speech data such as the duration of the spoken material. From these experiments, the system is found to produce satisfying identification scores for relatively short training and test speech segments.
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Sanders, Richard Calvin. "Voice recognition system implementation and laboratory exercise." Master's thesis, This resource online, 1995. http://scholar.lib.vt.edu/theses/available/etd-01262010-020212/.

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Ho, Ching-Hsiang. "Speaker modelling for voice conversion." Thesis, Brunel University, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.365076.

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Books on the topic "Voice recognition"

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Robert, Rodman, ed. Voice recognition. Boston: Artech House, 1997.

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Goldman, Thomas F. Voice Xpress: Basic skills in voice recognition. Upper Saddle River, NJ: Prentice Hall, 2001.

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Kutza, Patricia. Voice recognition: Technologies, markets, opportunities. Norwalk, CT: Business Communications Co., 2002.

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HOW DOES VOICE RECOGNITION WORK? New York, NY: Gareth Stevens Publishing, 2014.

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Baumgarten, Alan. Dragon NaturallySpeaking quicktorial: Voice recognition software. Cincinnati: South-Western Educational Pub., 2000.

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United States. Social Security Administration. Technology Assessment and Forecasting Group. ADP voice technology: Speech recognition and speech synthesis. [Washington, D.C.?]: U.S. Social Security Administration, 1985.

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Voice recognition: 21 poets for the 21st century. Tarset, Northumberland [England]: Bloodaxe Books, 2009.

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United States. Social Security Administration. Technology Assessment and Forecasting Group. ADP voice technology: Speech recognition and speech synthesis. [Washington, D.C.?]: U.S. Social Security Administration, 1985.

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North Atlantic Treaty Organization. Advisory Group for Aerospace Research and Development. Speech analysis and synthesis and man-machine speech communications for air operations. Neuilly sur Seine, France: AGARD, 1990.

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Markowitz, Judith A. Voice ID source profiles. [Evanston, IL]: J. Markowitz, 1997.

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Book chapters on the topic "Voice recognition"

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Mehta, Amit. "Voice Recognition." In PACS, 281–302. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/978-1-4757-3651-9_11.

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Mathias, Samuel Robert, and Katharina von Kriegstein. "Voice Processing and Voice-Identity Recognition." In Timbre: Acoustics, Perception, and Cognition, 175–209. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14832-4_7.

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Calvo, Andres. "Gesture and Voice Recognition." In Beginning Android Wearables, 349–79. Berkeley, CA: Apress, 2015. http://dx.doi.org/10.1007/978-1-4842-0517-4_10.

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Ananthapadmanabha, T. V. "Aerodynamic and Acoustic Theory of Voice Production." In Forensic Speaker Recognition, 309–63. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-0263-3_12.

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Jarng, Soon Suck. "Korean Voice Recognition System Development." In Lecture Notes in Electrical Engineering, 31–41. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-2598-0_4.

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Bahreini, Kiavash, Rob Nadolski, and Wim Westera. "FILTWAM and Voice Emotion Recognition." In Lecture Notes in Computer Science, 116–29. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12157-4_10.

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Wang, Houying, and Weiping Hu. "Optimization of Pathological Voice Feature Based on KPCA and SVM." In Biometric Recognition, 394–403. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12484-1_44.

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Otake, Yasutaka, Yasuhiro Tajima, and Matsuaki Terada. "A SIP-Based Voice-Mail System with Voice Recognition." In Lecture Notes in Computer Science, 985–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-25978-7_99.

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Lleida, E., J. B. Mariño, J. Salavedra, and A. Moreno. "Keyword Spotting, an Application for Voice Dialing." In Speech Recognition and Coding, 276–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-57745-1_40.

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Backes, Michael, Goran Doychev, Markus Dürmuth, and Boris Köpf. "Speaker Recognition in Encrypted Voice Streams." In Computer Security – ESORICS 2010, 508–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15497-3_31.

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Conference papers on the topic "Voice recognition"

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Chen, Shubo, Binsen Qian, and Harry Cheng. "Voice Recognition for STEM Education Using Robotics." In ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-68368.

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In this paper, we provide a new voice recognition framework which allows K-12 students to write programs to solve problems using voice control. The framework contains the voice recognition module SPHINX which is based on an open source machine learning tool developed by Carnegie Mellon University and a wrapper function which is written in C/C++ interpreter Ch. The wrapper function allows students to interact the module in Ch. Along with Ch programming and robotic coursework, students will get the chance to learn the basic concept of machine learning and voice recognition technique. In order to bring students attention and interest in machine learning, various tasks have been designed for students to accomplish based on the framework. The framework is also flexible for them to explore other interesting projects.
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Dittrich, Toby, and Sequoia Star. "Introducing Voice Recognition into Higher Education." In Fourth International Conference on Higher Education Advances. Valencia: Universitat Politècnica València, 2018. http://dx.doi.org/10.4995/head18.2018.8080.

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Abstract Voice Recognition (VR) software has now evolved to be fast and accurate enough to be useful in many educational settings. This paper describes two new uses for VR technology, both protected by patents, which can effectively address the lack of universal oral training in education today. The first use is Instant Note Capture (INC) which can be employed in live computer presentations and in an online software add-on tool called Incredible Classroom (IC) to place and store voice to text records in educational activities. The second is a new assessment tool called Virtual Oral Recitation Examination System (VORE) which enables oral discourse to be automatically and instantaneously assessed and used in new educational software tools requiring oral exercises. This paper identifies the necessity for and demonstrates the uses of voice recognition systems in education.
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Tymchenko, Oleksandr, Bohdana Havrysh, Oleksandr O. Tymchenko, Orest Khamula, Bohdan Kovalskyi, and Kateryna Havrysh. "Person Voice Recognition Methods." In 2020 IEEE Third International Conference on Data Stream Mining & Processing (DSMP). IEEE, 2020. http://dx.doi.org/10.1109/dsmp47368.2020.9204023.

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Dugdale, Anni, and Ben Kraal. "Magistrates and voice recognition." In the 20th conference of the computer-human interaction special interest group (CHISIG) of Australia. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1228175.1228199.

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Połap, Dawid. "Neuro-heuristic voice recognition." In 2016 Federated Conference on Computer Science and Information Systems. IEEE, 2016. http://dx.doi.org/10.15439/2016f128.

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Berdibaeva, Gulmira K., Oleg N. Bodin, Valery V. Kozlov, Dmitry I. Nefed'ev, Kasymbek A. Ozhikenov, and Yaroslav A. Pizhonkov. "Pre-processing voice signals for voice recognition systems." In 2017 18th International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices (EDM). IEEE, 2017. http://dx.doi.org/10.1109/edm.2017.7981748.

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Ye-Yi Wang. "Voice search - Information access via voice queries." In 2007 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU). IEEE, 2007. http://dx.doi.org/10.1109/asru.2007.4430095.

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Khotimah, Khusnul, Agus Budi Santoso, Miftahul Ma'arif, Alfiantin Noor Azhiimah, Bambang Suprianto, Meini Sondang Sumbawati, and Tri Rijanto. "Validation of Voice Recognition in Various Google Voice Languages using Voice Recognition Module V3 Based on Microcontroller." In 2020 Third International Conference on Vocational Education and Electrical Engineering (ICVEE). IEEE, 2020. http://dx.doi.org/10.1109/icvee50212.2020.9243184.

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Soon Suck Jarng. "HMM Voice Recognition Algorithm Coding." In 2011 International Conference on Information Science and Applications (ICISA 2011). IEEE, 2011. http://dx.doi.org/10.1109/icisa.2011.5772321.

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Higgins, Alan, L. Bahler, J. Porter, and P. Blais. "Robust matching for voice recognition." In SPIE's 1994 International Symposium on Optics, Imaging, and Instrumentation, edited by Richard J. Mammone and J. David Murley, Jr. SPIE, 1994. http://dx.doi.org/10.1117/12.191871.

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Reports on the topic "Voice recognition"

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Del Rose, Michael. Voice Digit Recognition Using Wavelets. Fort Belvoir, VA: Defense Technical Information Center, November 2004. http://dx.doi.org/10.21236/ada634136.

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Gadbois, Gregory J. Developing Multi-Voice Speech Recognition Confidence Measures and Applying Them to AHLTA-Mobile. Fort Belvoir, VA: Defense Technical Information Center, May 2011. http://dx.doi.org/10.21236/ada547479.

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Dennison, Thomas W., Frank J. Malkin, and Christopher C. Smyth. The Effect of Helicopter Vibration on the Accuracy of a Voice Recognition System. Fort Belvoir, VA: Defense Technical Information Center, September 1986. http://dx.doi.org/10.21236/ada174284.

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Novakoski, William L. Leveraging Technology: Using Voice Recognition to Improve Medical Records Production at Walter Reed Army Medical Center. Fort Belvoir, VA: Defense Technical Information Center, August 1999. http://dx.doi.org/10.21236/ada420777.

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Potts, Tavis, and Rebecca Ford. Leading from the front? Increasing Community Participation in a Just Transition to Net Zero in the North-East of Scotland. Scottish Universities Insight Institute, December 2022. http://dx.doi.org/10.57064/2164/19722.

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n line with Scottish Net Zero targets and the national strategy for a Just Transition, the Northeast of Scotland is transforming towards a low carbon future with a number of high-profile industry and policy initiatives. With the region home to global energy companies and historical high levels of energy sector employment, the narrative on transition is predominantly framed within an industrial and technological context, including narratives on new opportunities in green jobs, green industrial development, technical innovation and new infrastructure to support energy transition. As the energy landscape shifts in the North-East of Scotland, the impacts will be felt most keenly in communities from shifts in employment to changes to local supply chains. It is important to note that Net Zero ambitions will also change the nature and structure of communities in the region, for those within a shifting oil and gas industry and those without. A just transition ensures that all voices are heard, engaged and included in the process of change, and that communities, including those who have benefited and those who have not, have a stake in determining the direction of travel of a changing society and economy of the North-east. As a result, there is a need for a community-oriented perspective to transition which discusses a range of values and perspectives, the opportunities and resources available for transition and how communities of place can support the process of change toward Net Zero. Social transformation is a key element of a just transition and community engagement, inclusion and participation is embedded in the principles laid down by the Just Transition Commission. Despite this high-level recognition of social justice and inclusion at the heart of transition, there has been little move to understand what a just transition means in the context of local communities in the NorthEast. This project aims to address this imbalance and promote the ability of communities to not only engage but to help steer net zero transitions. It seeks to uncover and build a stronger local consensus about the vision and pathways for civil society to progress a just transition in the Northeast of Scotland. The project aims to do this through bringing together civil society, academic, policy and business stakeholders across three interactive workshops to: 1. Empower NE communities to engage with the Just Transition agenda 2. Identify what are the key issues within a Just Transition and how they can be applied in the Northeast. 3. Directly support communities by providing training and resources to facilitate change by working in partnership. The project funding supported the delivery of three professionally facilitated online workshops that were held over 2021/22 (Figure 1). Workshop 1 explored the global principles within a just transition and how these could apply to the Scottish context. Workshop 2 examined different pathways and options for transition in the context of Northeast Scotland. Workshop 3, in partnership with NESCAN explored operational challenges and best practices with community participants. The outcomes from the three workshops are explored in detail.
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Shifting power in global health: Decolonising discourses - series synthesis. United Nations University - International Institute for Global Health, Development Reimagined, Wilton Park, 2022. http://dx.doi.org/10.37941/mr-f/2022/3.

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There have been an increasing number of voices – both individual and institutional – that have called for a reassessment of global health and greater recognition of its colonial heritage. Whilst there is currently no unified definition of what it would mean to decolonise global health, in its broadest sense, it has been described as the ‘imperative of problematising coloniality'. It is within this context that the “Shifting Power in Global Health: Decolonising Discourses” series was co-convened by the United Nations University’s International Institute for Global Health, Development Reimagined, and Wilton Park. Held as a set of three dialogues between November 2021 and May 2022, the series took as its point of departure the many discussions, webinars, and publications presenting the ways coloniality manifests within global health, with the aim of shifting from problematising coloniality to catalysing decoloniality. While colonialism refers to the physical occupation of a bounded territory, coloniality, in both its historical and present-day manifestations, is understood as a globally persistent and geographically unbounded extractive process that drives inequities. Consequently, while decolonisation is easily recognised by the physical removal or exit of the colonising force, a similarly straightforward definition for decoloniality is not so easily found.
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