Relevant bibliographies by topics / Speech forensics

Academic literature on the topic 'Speech forensics'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Speech forensics"

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Lei, Lei, and She Kun. "Speaker Recognition Using Wavelet Cepstral Coefficient, I-Vector, and Cosine Distance Scoring and Its Application for Forensics." Journal of Electrical and Computer Engineering 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/4908412.

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An important application of speaker recognition is forensics. However, the accuracy of speaker recognition in forensic cases often drops off rapidly because of the ill effect of ambient noise, variable channel, different duration of speech data, and so on. Therefore, finding a robust speaker recognition model is very important for forensics. This paper builds a new speaker recognition model based on wavelet cepstral coefficient (WCC), i-vector, and cosine distance scoring (CDS). This model firstly uses the WCC to transform the speech into spectral feature vecors and then uses those spectral feature vectors to train the i-vectors that represent the speeches having different durations. CDS is used to compare the i-vectors to give out the evidence. Moreover, linear discriminant analysis (LDA) and the within-class covariance normalization (WCNN) are added to the CDS algorithm to deal with the channel variability problem. Finally, the likelihood ratio estimates the strength of the evidence. We use the TIMIT database to evaluate the performance of the proposed model. The experimental results show that the proposed model can effectively solve the troubles of forensic scenario, but the time cost of the method is high.
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Garcia‐Romero, Daniel, and Carol Espy‐Wilson. "Speech forensics: Automatic acquisition device identification." Journal of the Acoustical Society of America 127, no. 3 (March 2010): 2044. http://dx.doi.org/10.1121/1.3385386.

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Muhammad, Ghulam, and Khalid Alghathbar. "Environment Recognition for Digital Audio Forensics Using MPEG-7 and MEL Cepstral Features." Journal of Electrical Engineering 62, no. 4 (July 1, 2011): 199–205. http://dx.doi.org/10.2478/v10187-011-0032-0.

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Environment Recognition for Digital Audio Forensics Using MPEG-7 and MEL Cepstral FeaturesEnvironment recognition from digital audio for forensics application is a growing area of interest. However, compared to other branches of audio forensics, it is a less researched one. Especially less attention has been given to detect environment from files where foreground speech is present, which is a forensics scenario. In this paper, we perform several experiments focusing on the problems of environment recognition from audio particularly for forensics application. Experimental results show that the task is easier when audio files contain only environmental sound than when they contain both foreground speech and background environment. We propose a full set of MPEG-7 audio features combined with mel frequency cepstral coefficients (MFCCs) to improve the accuracy. In the experiments, the proposed approach significantly increases the recognition accuracy of environment sound even in the presence of high amount of foreground human speech.
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Renza, Diego, Jaisson Vargas, and Dora M. Ballesteros. "Robust Speech Hashing for Digital Audio Forensics." Applied Sciences 10, no. 1 (December 28, 2019): 249. http://dx.doi.org/10.3390/app10010249.

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The verification of the integrity and authenticity of multimedia content is an essential task in the forensic field, in order to make digital evidence admissible. The main objective is to establish whether the multimedia content has been manipulated with significant changes to its content, such as the removal of noise (e.g., a gunshot) that could clarify the facts of a crime. In this project we propose a method to generate a summary value for audio recordings, known as hash. Our method is robust, which means that if the audio has been modified slightly (without changing its significant content) with perceptual manipulations such as MPEG-4 AAC, the hash value of the new audio is very similar to that of the original audio; on the contrary, if the audio is altered and its content changes, for example with a low pass filter, the new hash value moves away from the original value. The method starts with the application of MFCC (Mel-frequency cepstrum coefficients) and the reduction of dimensions through the analysis of main components (principal component analysis, PCA). The reduced data is encrypted using as inputs two values from a particular binarization system using Collatz conjecture as the basis. Finally, a robust 96-bit code is obtained, which varies little when perceptual modifications are made to the signal such as compression or amplitude modification. According to experimental tests, the BER (bit error rate) between the hash value of the original audio recording and the manipulated audio recording is low for perceptual manipulations, i.e., 0% for FLAC and re-quantization, 1% in average for volume (−6 dB gain), less than 5% in average for MPEG-4 and resampling (using the FIR anti-aliasing filter); but more than 25% for non-perceptual manipulations such as low pass filtering (3 kHz, fifth order), additive noise, cutting and copy-move.
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Wafula, George, and Andrew M. "Social Media Forensics for Hate Speech Opinion Mining." International Journal of Computer Applications 155, no. 1 (December 15, 2016): 39–47. http://dx.doi.org/10.5120/ijca2016912258.

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Zhang, Yujin, Shuxian Dai, Wanqing Song, Lijun Zhang, and Dongmei Li. "Exposing Speech Resampling Manipulation by Local Texture Analysis on Spectrogram Images." Electronics 9, no. 1 (December 25, 2019): 23. http://dx.doi.org/10.3390/electronics9010023.

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Speech tampering may be aided by the resampling operation. It is significant for speech forensics to effectively detect the resampling; however, there are few studies on speech resampling detection. The purpose of this paper was therefore to provide a new training ideal to detect speech resampling. After resampling, the speech signal changes regularly in the time–frequency domain. In this paper, we theoretically analyzed the corresponding relationship between time domain and frequency domain of the resampled speech. Compared with the original speech, the bandwidth of resampled speech was stretched or compressed. First, the spectrogram was generated by short-time Fourier transform (STFT) from the speech. Then, the local binary pattern (LBP) operator was applied to model the statistical changes in the spectrogram and the LBP histogram was calculated as discriminative features. Finally, a support vector machine (SVM) was applied to classify the developed features to identify whether the speech had undergone the resampling operation. The experimental results show that the proposed method has superior detection performance in different resampling scenarios than some existing methods, and the proposed features are very robust against the commonly used compression post-processing operation. This highlights the promising potential of the proposed method as a speech resampling detection tool in practical forensics applications.
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Liu, Zhenghui, Jiwu Huang, Xingming Sun, and Chuanda Qi. "A security watermark scheme used for digital speech forensics." Multimedia Tools and Applications 76, no. 7 (April 21, 2016): 9297–317. http://dx.doi.org/10.1007/s11042-016-3533-9.

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Jiang, Yi, and Dengpan Ye. "Black-Box Adversarial Attacks against Audio Forensics Models." Security and Communication Networks 2022 (January 17, 2022): 1–8. http://dx.doi.org/10.1155/2022/6410478.

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Speech synthesis technology has made great progress in recent years and is widely used in the Internet of things, but it also brings the risk of being abused by criminals. Therefore, a series of researches on audio forensics models have arisen to reduce or eliminate these negative effects. In this paper, we propose a black-box adversarial attack method that only relies on output scores of audio forensics models. To improve the transferability of adversarial attacks, we utilize the ensemble-model method. A defense method is also designed against our proposed attack method under the view of the huge threat of adversarial examples to audio forensics models. Our experimental results on 4 forensics models trained on the LA part of the ASVspoof 2019 dataset show that our attacks can get a 99 % attack success rate on score-only black-box models, which is competitive to the best of white-box attacks, and 60 % attack success rate on decision-only black-box models. Finally, our defense method reduces the attack success rate to 16 % and guarantees 98 % detection accuracy of forensics models.
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Zuhriyanto, Ikhsan, Anton Yudhana, and Imam Riadi. "Comparative analysis of Forensic Tools on Twitter applications using the DFRWS method." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 4, no. 5 (October 30, 2020): 829–36. http://dx.doi.org/10.29207/resti.v4i5.2152.

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Current crime is increasing, one of which is the crime of using social media, although no crime does not leave digital evidence. Twitter application is a social media that is widely used by its users. Acts of crime such as fraud, insults, hate speech, and other crimes lately use many social media applications, especially Twitter. This research was conducted to find forensic evidence on the social media Twitter application that is accessed using a smartphone application using the Digital Forensics Research Workshop (DFRWS) method. These digital forensic stages include identification, preservation, collection, examination, analysis, and presentation in finding digital evidence of crime using the MOBILedit Forensic Express software and Belkasoft Evidence Center. Digital evidence sought on smartphones can be found using case scenarios and 16 variables that have been created so that digital proof in the form of smartphone specifications, Twitter accounts, application versions, conversations in the way of messages and status. This study's results indicate that MOBILedit Forensic Express digital forensic software is better with an accuracy rate of 85.75% while Belkasoft Evidence Center is 43.75%.
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Rokanatnam, Thurgeaswary, and Hazinah Kutty Mammi. "Study on Gender Identification Based on Audio Recordings Using Gaussian Mixture Model and Mel Frequency Cepstrum Coefficient Technique." International Journal of Innovative Computing 11, no. 2 (October 31, 2021): 35–41. http://dx.doi.org/10.11113/ijic.v11n2.343.

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Speaker recognition is an ability to identify speaker’s characteristics based from spoken language. The purpose of this study is to identify gender of speakers based on audio recordings. The objective of this study is to evaluate the accuracy rate of this technique to differentiate the gender and also to determine the performance rate to classify even when using self-acquired recordings. Audio forensics uses voice recordings as part of evidence to solve cases. This study is mainly conducted to provide an easier technique to identify the unknown speaker characteristics in forensic field. This experiment is fulfilled by training the pattern classifier using gender dependent data. In order to train the model, a speech database is obtained from an online speech corpus comprising of both male and female speakers. During the testing phase, apart from the data from speech corpus, audio recordings of UTM students will too be used to determine the accuracy rate of this speaker identification experiment. As for the technique to run this experiment, Mel Frequency Cepstrum Coefficient (MFCC) algorithm is used to extract the features from speech data while Gaussian Mixture Model (GMM) is used to model the gender identifier. Noise removal was not used for any speech data in this experiment. Python software is used to extract using MFCC coefficients and model the behavior using GMM technique. Experiment results show that GMM-MFCC technique can identify gender regardless of language but with varying accuracy rate.
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Dissertations / Theses on the topic "Speech forensics"

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Becker, Robert Roy. "The Narrative of the Professional: The Value of Collegiate Forensics Participation." Diss., North Dakota State University, 2019. https://hdl.handle.net/10365/29513.

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Forensics, or competitive speech and debate, has a history stretching back to the ancient Greeks. Although practitioners, students, and coaches have long sung its praises, limited research has been done to demonstrate the long-term value of forensics competition for students. This study used narrative interviews to discover the perceived value of forensics competition to individuals who were at least ten years removed from competition and had not remained active in forensics. After interviewing 34 individuals, this study used grounded theory (Glaser, 1965; 2002; Glaser & Strauss, 1967) to analyze the results. Analysis revealed that individuals followed a similar pattern of becoming involved in forensics and remaining as participants. Additionally, they believed they learned academic skills, social skills, and had more opportunities because of their participation in forensics, despite having to overcome some negative effects of participation. Participants noted that they used many of the skills they developed in forensics every day. Participants also demonstrated that forensics was a part of their identity and many remained connected to former teammates, former competitors, and their alma mater. Analysis led to the development of the Narrative of the Professional, which is the story of the forensics competitor.
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Singh, Latchman. "Speech enhancement for forensic applications." Thesis, Queensland University of Technology, 1998. https://eprints.qut.edu.au/36080/1/36080_Singh_1998.pdf.

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Forensic audio recordings are usually made with a single covert microphone in non-ideal conditions. In non-ideal conditions the recordings are highly susceptible to various types of noise. The noise is usually broadband noise, co-talker interference, impulsive noise, narrow band noise or convolutional noise. There are existing speech enhancement techniques available to suppress most of the noise types mentioned, but when the noise is of a considerable level the performance of most enhancement techniques tend to decrease significantly. This thesis presents a study of speech enhancement techniques that are applicable to the enhancement of forensic audio recordings or that can be used in a forensic recording environment. It considers both pre-processing and post-processing speech enhancement techniques. This thesis investigates the improvement of some of the existing speech enhancement techniques as well as proposing some new ones. The performance of the improved and proposed speech enhancement techniques were evaluated objectively using the segmental signal-to-noise ratio (SNRseg) and subjectively using the mean opinion score (MOS). A review of the current speech enhancement techniques is presented in the thesis and is also used as a reference in some comparisons. The current speech enhancement techniques considered are those that are applicable to forensic audio recordings. The performance of the existing techniques are assessed in the comparisons with the speech enhancement techniques proposed by this thesis. Two pre-processing speech enhancement techniques are presented in this thesis. The first pre-processing speech enhancement technique is designed to improve existing broadband noise suppression techniques by the use of frequency shift keying (FSK) signals. It is based on a simple concept, which is to insert a known tone of sufficient amplitude into the silent segments of a speech signal prior to transmission. At the receiver the detection of silent or non-speech segments used in estimating the noise, becomes a simpler and more accurate task due to the inserted tone. The second pre-processing speech enhancement technique is designed to suppress a wide range of noises and it is based on zero padding. Zero padding involves inserting a zero value sample in between each speech signal sample prior to transmission. The inserted zero value samples allow accurate characterisation of the noise in the adjacent speech samples. At the receiver the noise is estimated from the sample positions allocated for the zero value samples. Several post-processing speech enhancement techniques are presented in this thesis. The first post-processing speech enhancement technique is designed for the suppression of co-talker interference and it uses a combination of dynamic time warping (DTW) and dual channel adaptive filtering. This technique is proposed for the suppression of co-talker interference, when the co-talker interference or noise reference signal is obtainable at a later instance as in the case of many covert forensic recordings. The corrupted speech signal and the noise reference signal are aligned using DTW and then the co-talker interference is suppressed using a dual channel adaptive filter. The second post-processing speech enhancement technique is designed for broadband noise suppression and is based on spectral subtraction but it incorporates the masking properties of the human auditory system for improved performance. Auditory masking is used to find the masking threshold, below which the noise is no longer perceivable. Only those noise components above the masking threshold are suppressed. This approach is taken to reduce any byproducts such as musical noise. The third post-processing speech enhancement technique is designed for broadband noise suppression and is based on spectral subtraction but it exploits the human auditory systems perception of frequency. Critical band analysis is used to group frequencies that are similarly perceived, which are then treated as a single entity by the enhancement technique.
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Barger, Peter James. "Speech processing for forensic applications." Thesis, Queensland University of Technology, 1998. https://eprints.qut.edu.au/36081/1/36081_Barger_1998.pdf.

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This thesis examines speech processing systems appropriate for use in forensic analysis. The need for automatic speech processing systems for forensic use is justified by the increasing use of electronically recorded speech for communication. An automatic speaker identification and verification system is described which was tested on data gathered by the Queensland Police Force. Speaker identification using Gaussian mixture models (GMMs) is shown to be useful as an indicator of identity, but not sufficiently accurate to be used as the sole means of identification. It is shown that training GMMs on speech of one language and testing on speech of another language introduces significant bias into the results, which is unpredictable in its effects. This has implications for the performance of the system on subjects attempting to disguise their voices. Automatic gender identification systems are shown to be highly accurate, attaining 98% accuracy, even with very simple classifiers, and when tested on speech degraded by coding or reverberation. These gender gates are useful as initial classifiers in a larger speaker classification system and may even find independent use in a forensic environment. A dual microphone method of improving the performance of speaker identification systems in noisy environments is described. The method gives a significant improvement in log-likelihood scores when its output is used as input to a GMM. This implies that speaker identification tests may be improved in accuracy. A method of automatically assessing the quality of transmitted speech segments using a classification scheme is described. By classifying the difference between cepstral parameters describing the original speech and the transmitted speech, an estimate of the speech quality is obtained.
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Fisher, Andrew John. "Speech enhancement for forensic applications." Thesis, Queensland University of Technology, 1995. https://eprints.qut.edu.au/36243/1/36243_Fisher_1995.pdf.

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Law enforcement agencies often engage in surveillance operations which involve the recording of spoken conversations. As is often the case, these recordings are made with a single microphone under covert conditions. Under this non-ideal situation, the speech signal is highly susceptible to be severely corrupted by various forms of noise, the most common of which is broadband in nature. This thesis presents a study conducted to investigate the enhancement of speech recordings for forensic applications. A new speech enhancement scheme has been proposed here, to provide noise reduction without compromising the intelligibility of the speech. The scheme implements a hybrid approach combining both spectral and root-cepstral subtraction. Extensive testing using both subjective and objective based intelligibility and acceptability assessment schemes, indicate that the system is successful in providing intelligibility improvement and superior signal-to-noise ratio with minimal spectral distortion. In addition, the proposed system was also tested in the capacity as a preprocessing stage to other speech applications such as speech recognition, speaker recognition and speech coding. The system proved to be beneficial for speech coding, while application to the recognition techniques was limited despite showing positive potential. Finally the system was implemented in real-time and was found additionally successful when applied to enhancement of speech transmitted over High Frequency communication channels.
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Phythian, Mark. "Speaker identification for forensic applications." Thesis, Queensland University of Technology, 1998. https://eprints.qut.edu.au/36079/3/__qut.edu.au_Documents_StaffHome_StaffGroupR%24_rogersjm_Desktop_36079_Digitised%20Thesis.pdf.

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A major application of Speaker Identification (SI) is suspect identification by voice. This thesis investigates techniques that can be used to improve SI technology as applied to suspect identification. Speech Coding techniques have become integrated into many of our modern voice communications systems. This prompts the question - how are automatic speaker identification systems and modern forensic identification techniques affected by the introduction of digitally coded speech channels? Presented in this thesis are three separate studies investigating the effects of speech coding and compression on current speaker recognition techniques. A relatively new Spectral Analysis technique - Higher Order Spectral Analysis (HOSA) - has been identified as a potential candidate for improving some aspects of forensic speaker identification tasks. Presented in this thesis is a study investigating the application of HOSA to improve the robustness of current ASR techniques in the presence of additive Gaussian noise. Results from our investigations reveal that incremental improvements in each of these aspects related to automatic and forensic identification are achievable.
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Rhodes, Richard William. "Assessing the strength of non-contemporaneous forensic speech evidence." Thesis, University of York, 2012. http://etheses.whiterose.ac.uk/3935/.

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The aim of this thesis is to assess the impact of long term non-contemporaneity on the strength of forensic speech evidence. Speakers experience age-related changes to the voice over long delays and this time also presents the opportunity for social factors to vary. These changes are shown to impact on speech parameters used in forensic analyses. Using longitudinal data from the Up documentary series, this thesis analyses the effects of aging on forensically useful acoustic parameters in eight speakers at five seven-year intervals between ages 21 and 49. The investigation reveals significant age-related changes in real-time across adulthood. Frequencies of the first three formants in monophthongs /i: ɪ e a ɑ: ʌ ɒ ʊ & u:/ and diphthongs /eɪ & aɪ/ show comprehensive reduction. For monophthongs, F1 exhibits mean change of 8.5%, greater than F2 and F3 at 3.7% and 2.2% respectively. Vowel quality also impacts on magnitude of change in each formant. Estimations based on this data suggest that vocal tract extension and restricted articulator movement are probable drivers for acoustic change, operating on different timelines. Counter-examples to this aging pattern can generally be explained by social factors, as a result of mobility or in accordance with mainstream changes in a variety. Strength of evidence estimates for these non-contemporaneous data are calculated using a numerical likelihood ratio (LR) approach. Age-related changes result in weaker and fewer correct LRs with greater length delays. Cubic coefficients of diphthong formants are investigated in line with a formant dynamic approach. These LR tests show promising results and resilience to aging, especially in F1; tentatively suggesting that, for these speakers, some speaker-specific behaviour pervades in spite of physiological changes. This analysis raises several questions with regards to applying an overtly numerical LR approach where there is apparent mismatch between forensic samples. The effect of aging on an ASR system (BATVOX) is also tested for six male subjects. The system measures Mel-frequency cepstral coefficient (MFCC) parameters that reflect the physical properties of the vocal tract. Predicted degradation of the system’s performance with increasing age is apparent. The reduction in performance is significant, varies between speakers, and is striking in longer delays for all speakers. The degradation in strength of evidence for acoustic data from monophthongs and formant dynamic coefficients, as well as that for the ASR system, demonstrates that aging presents a real problem for forensic analysis in non-contemporaneous cases. Furthermore, aging also presents issues for speech databases for the purpose of assessing strength of evidence, where further research into distributions of parameters in different age groups is warranted.
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Khodai-Joopari, Mehrdad Information Technology &amp Electrical Engineering Australian Defence Force Academy UNSW. "Forensic speaker analysis and identification by computer : a Bayesian approach anchored in the cepstral domain." Awarded by:University of New South Wales - Australian Defence Force Academy. School of Information Technology and Electrical Engineering, 2007. http://handle.unsw.edu.au/1959.4/38715.

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This thesis advances understanding of the forensic value of the automatic speech parameters by addressing the following question: what is the potentiality of the speech cepstrum as a forensic-acoustic parameter? Despite many advances in automatic speech and speaker recognition, robust and unconstrained progress in technical forensic speaker identification has been partly impeded by our incomplete understanding of the interaction and relation between forensic phonetics and the techniques employed in state-of-the-art automatic speech and speaker recognition. The posed question underlies the recurrent and longstanding issue of acoustic parameterisation in the area of forensic phonetics, where 1) speaker identification often must be carried out under less than optimal conditions, and 2) views differ on the usefulness and trustworthiness of the formant frequency measurements. To this end, a new formulation for the forensic evaluation of speech data was derived which is effectively a spectral likelihood ratio with enhanced sensitivity to the local peaks of the formant structure of the speech spectrum of vowel sounds, while retaining the characteristics of the Bayesian framework. This new hybrid formula was used together with a novel approach, which is founded on a statistically-based matched-pairs technique to account for various levels of variation inherent in speech recordings, thereby providing a spectrally meaningful measure of variations between two speech spectra and hence the true worth of speech samples as forensic evidence. The experimental results are obtained based on a forensically-realistic database of a relatively large population of 297 native speakers of Japanese. In sum, the research conducted in this thesis is a major step forward in advancing the forensic-phonetic field which broadens the objective basis of the forensic speaker identification. Beyond advancing knowledge in the field, the semi data-independent nature of the new formula ultimately has great implications in technical forensic speaker identification. It also provides us with a valuable biometric tool with both academic and commercial potential in crime investigation in a field which is already suffering from the lack of adequate data.
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Gavaldà, Ferré Núria. "Index of idiolectal similitude for the phonological module of English applied to forensic speech comparison." Doctoral thesis, Universitat Pompeu Fabra, 2013. http://hdl.handle.net/10803/123775.

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The framework of the present PhD dissertation is the area that results from the overlap between the field of variationist sociolinguistics and forensic linguistics, which mainly concerns the study of variation between different individuals –inter-speaker variation– and variation within a single individual –intra-speaker variation– for forensic purposes. The primary objective of the present dissertation is twofold. On the one hand, it proposes a protocol for the creation of an Index of Idiolectal Similitude (IIS) for the phonological module of English that can effectively determine whether two oral samples show inter-speaker variation –which would indicate that the samples have been produced by two different individuals– or intra-speaker variation –which would allow to conclude that the samples have been produced by the same individual. On the other hand, the analysis of the fourteen variables proposed in a corpus that contains data on sixteen speakers and that is stratified according to measurement time –as a result of a real time study–, language contact and gender, provides an important contribution to the Base Rate knowledge, which constitutes one of the main challenges of current forensic linguistics. Results show that inter-speaker variation is generally higher than intra-speaker variation, and that a speaker’s idiolectal style remains relatively stable over time. Therefore, the IIS is presented as an innovative quantitative tool which, together with other quantitative and qualitative techniques that the linguist acting as expert witness may have at their disposition, can help reach a conclusion regarding the probability of two samples having been produced or not by the same speaker.
Aquesta tesi doctoral s’emmarca dins l’àrea comú on es troben els camps de la sociolingüística de la variació i la lingüística forense, en la qual es troba l’estudi de la variació entre diferents individus –variació inter-parlant– i la variació en del mateix individu –variació intra-parlant– amb finalitats forenses. La investigació té dos objectius principals. D’una banda, es proposa el protocol per a la creació d’un Índex de Similitud Idiolectal (ISI) per al mòdul fonològic de l’anglès que pot determinar de manera efectiva si dues mostres orals mostren variació inter-parlant –que indicaria que les mostres haurien estat produïdes per dos individus diferents– o variació intra-parlant –la qual cosa portaria a concloure que les mostres haurien estat produïdes pel mateix individu. D’altra banda, l’anàlisi de les catorze variables proposades en un corpus que conté setze parlants i que està estratificat per temps de mesura –com a resultat d’un estudi en temps real–, contacte de llengües i gènere biològic, comporta una contribució important a la referència de distribució poblacional (Base Rate Knowledge) que constitueix un dels grans reptes de la lingüística forense actual. Els resultats mostren que la variació inter-parlant és generalment més alta que la intra-parlant, i que l’estil idiolectal d’un individu es manté relativament estable malgrat el pas del temps. Per tant, l’ISI es presenta com una eina quantitativa innovadora que, juntament amb altres tècniques quantitatives i qualitatives que el lingüista forense pot tenir a la seva disposició, pot ajudar a prendre una decisió sobre la probabilitat que dues mostres hagin estat produïdes o no pel mateix parlant.
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Al-Ali, Ahmed Kamil Hasan. "Forensic speaker recognition under adverse conditions." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/130783/1/Ahmed%20Kamil%20Hasan_Al-Ali_Thesis.pdf.

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The performance of forensic speaker recognition systems degrades significantly in the presence of environmental noise and reverberant conditions. This research developed new techniques to improve forensic speaker recognition performance under these conditions using fusion feature extraction techniques and speech enhancement based on the independent component analysis algorithm. A range of forensic speaker recognition applications will benefit from the research outcomes including criminal investigations and law enforcement agencies.
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Flory, Yvonne. "The impact of head and body postures on the acoustic speech signal." Thesis, University of Cambridge, 2015. https://www.repository.cam.ac.uk/handle/1810/247436.

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This dissertation is aimed at investigating the impact of postural changes within speakers on the acoustic speech signal to complement research on articulatory changes under the same conditions. The research is therefore relevant for forensic phonetics, where quantifying within-speaker variation is vital for the accuracy of speaker comparison. To this end, two acoustic studies were carried out to quantify the influence of five head positions and three body orientations on the acoustic speech signal. Results show that there is a consistent change in the third formant, a change which was most evident in the body orientation measurements, and to a lesser extent in the head position data. Analysis of the results with respect to compensation strategies indicates that speakers employ different strategies to compensate for these perturbations to their vocal tract. Some speakers did not exhibit large differences in their speech signal, while others appeared to compensate much less. Across all speakers, the effect was much stronger in what were deemed ‘less natural’, postures. That is, speakers were apparently less able to predict and compensate for the impact of prone body orientation on their speech than for that of the more natural supine orientation. In addition to the acoustic studies, a perception experiment assessed whether listeners could make use of acoustic cues to determine the posture of the speaker. Stimuli were chosen with, by design, stronger or weaker acoustic cues to posture, in order to elicit a possible difference in identification performance. Listeners were nevertheless not able to identify above chance whether a speaker was sitting or lying in prone body orientation even when hearing the set with stronger cues. Further combined articulatory and acoustic research will have to be carried out to disentangle which articulatory behaviours correlate with the acoustic changes presented in order to draw a more comprehensive picture of the effects of postural variation on speech.
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Books on the topic "Speech forensics"

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Oberg, Brent C. Forensics: The winner's guide to speech contests. Colorado Springs, Colo: Meriwether Pub., 1995.

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Littlefield, Robert S. Voices on the prairie: Bringing speech and theatre to North Dakota. Fargo, N.D: Institute for Regional Studies, North Dakota State University, 1998.

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Austin-Lett, Genelle. How to write the perfect ballot: A handbook for judging high school speech and debate tournaments. Dubuque, Iowa: Kendall/Hunt Pub., 1998.

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Sencar, Husrev T. Digital Image Forensics: There is More to a Picture than Meets the Eye. New York, NY: Springer New York, 2013.

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Zyubina, Irina. Prosecutors' forensic speech in implicit pragmalinguistics. Newcastle upon Tyne: Cambridge Scholars Pub., 2011.

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Zyubina, Irina. Prosecutors' forensic speech in implicit pragmalinguistics. Newcastle upon Tyne: Cambridge Scholars Pub., 2011.

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McIntosh, Kenneth. A stranger's voice: Forensic speech identification. Philadelphia: Mason Crest Publishers, 2009.

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Hyperides. Hypereides: The forensic speeches. Oxford: Oxford University Press, 2000.

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A, Galizio Lawrence, ed. Elements of parliamentary debate: A guide to public argument. New York: Longman, 1999.

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Stump speech murder: A Pamela Barnes acoustic mystery. [Aurora, IL]: Cozy Cat Press, 2012.

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Book chapters on the topic "Speech forensics"

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Zjalic, James. "Speech forensics." In Digital Audio Forensics Fundamentals, 125–31. Abingdon, Oxon; New York, NY: Routledge, 2021. | Series: Audio Engineering Society presents: Focal Press, 2020. http://dx.doi.org/10.4324/9780429292200-9.

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Huang, Ting, Hongxia Wang, Yi Chen, and Peisong He. "GRU-SVM Model for Synthetic Speech Detection." In Digital Forensics and Watermarking, 115–25. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43575-2_9.

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Qian, Qing, Hongxia Wang, Sani M. Abdullahi, Huan Wang, and Canghong Shi. "Speech Authentication and Recovery Scheme in Encrypted Domain." In Digital Forensics and Watermarking, 46–60. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53465-7_4.

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Wang, Hongxia, Linna Zhou, Wei Zhang, and Shuang Liu. "Watermarking-Based Perceptual Hashing Search Over Encrypted Speech." In Digital-Forensics and Watermarking, 423–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-43886-2_30.

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Miao, Haibo, Liusheng Huang, Yao Shen, Xiaorong Lu, and Zhili Chen. "Steganalysis of Compressed Speech Based on Markov and Entropy." In Digital-Forensics and Watermarking, 63–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-43886-2_5.

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Gong, Chen, Xiaowei Yi, and Xianfeng Zhao. "Pitch Delay Based Adaptive Steganography for AMR Speech Stream." In Digital Forensics and Watermarking, 275–89. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11389-6_21.

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Wang, Donghua, Rangding Wang, Li Dong, Diqun Yan, and Yiming Ren. "Efficient Generation of Speech Adversarial Examples with Generative Model." In Digital Forensics and Watermarking, 251–64. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69449-4_19.

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Capolupo, Daniele, and Fabrizio d’Amore. "Synthetic Speech Detection and Audio Steganography in VoIP Scenarios." In Digital-Forensics and Watermarking, 145–59. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31960-5_13.

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Sun, Fang, Yanli Li, Zhenghui Liu, and Chuanda Qi. "Speech Forensics Based on Sample Correlation Degree." In Advances in Intelligent Systems and Computing, 173–83. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0344-9_14.

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Farouk, Mohamed Hesham. "Steganography, Forensics, and Security of Speech Signal." In SpringerBriefs in Electrical and Computer Engineering, 71–76. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69002-5_13.

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Conference papers on the topic "Speech forensics"

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McKay, Christine, Ashwin Swaminathan, Hongmei Gou, and Min Wu. "Image acquisition forensics: Forensic analysis to identify imaging source." In ICASSP 2008 - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2008. http://dx.doi.org/10.1109/icassp.2008.4517945.

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Stamm, Matthew C., W. Sabrina Lin, and K. J. Ray Liu. "Forensics vs. anti-forensics: A decision and game theoretic framework." In ICASSP 2012 - 2012 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2012. http://dx.doi.org/10.1109/icassp.2012.6288237.

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Palfy, Juraj, Sakhia Darjaa, and Jiri Pospichal. "Dysfluent speech detection by image forensics techniques." In 2013 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU). IEEE, 2013. http://dx.doi.org/10.1109/asru.2013.6707712.

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Malik, Hafiz, and Hany Farid. "Audio forensics from acoustic reverberation." In 2010 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2010. http://dx.doi.org/10.1109/icassp.2010.5495479.

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Stamm, Matthew C., Steven K. Tjoa, W. Sabrina Lin, and K. J. Ray Liu. "Anti-forensics of JPEG compression." In 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2010. IEEE, 2010. http://dx.doi.org/10.1109/icassp.2010.5495491.

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Wang, Wenxiu, Yunxia Zhou, Jingkai Zhou, and Dongjian Cao. "Television station identification system based on speech forensics." In 2nd International Conference on Information, Electronics and Computer. Paris, France: Atlantis Press, 2014. http://dx.doi.org/10.2991/icieac-14.2014.33.

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Barni, M., G. Cancelli, and A. Esposito. "Forensics aided steganalysis of heterogeneous images." In 2010 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2010. http://dx.doi.org/10.1109/icassp.2010.5495494.

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Wu, Zhung-Han, Matthew C. Stamm, and K. J. Ray Liu. "Anti-forensics of median filtering." In ICASSP 2013 - 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2013. http://dx.doi.org/10.1109/icassp.2013.6638217.

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Lin, W. Sabrina, and K. J. Ray Liu. "Modulation forensics for wireless digital communications." In ICASSP 2008. IEEE International Conference on Acoustic, Speech and Signal Processes. IEEE, 2008. http://dx.doi.org/10.1109/icassp.2008.4517978.

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Tuononen, Marko, Rosa Gonzalez Hautamaki, and Pasi Fränti. "Automatic Voice Activity Detection in Different Speech Applications." In 1st International ICST Conference on Forensic Applications and Techniques in Telecommunications, Information and Multimedia. ACM, 2008. http://dx.doi.org/10.4108/e-forensics.2008.2781.

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Reports on the topic "Speech forensics"

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Issues in Data Processing and Relevant Population Selection. OSAC Speaker Recognition Subcommittee, November 2022. http://dx.doi.org/10.29325/osac.tg.0006.

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Abstract:
In Forensic Automatic Speaker Recognition (FASR), forensic examiners typically compare audio recordings of a speaker whose identity is in question with recordings of known speakers to assist investigators and triers of fact in a legal proceeding. The performance of automated speaker recognition (SR) systems used for this purpose depends largely on the characteristics of the speech samples being compared. Examiners must understand the requirements of specific systems in use as well as the audio characteristics that impact system performance. Mismatch conditions between the known and questioned data samples are of particular importance, but the need for, and impact of, audio pre-processing must also be understood. The data selected for use in a relevant population can also be critical to the performance of the system. This document describes issues that arise in the processing of case data and in the selections of a relevant population for purposes of conducting an examination using a human supervised automatic speaker recognition approach in a forensic context. The document is intended to comply with the Organization of Scientific Area Committees (OSAC) for Forensic Science Technical Guidance Document.
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