Strength of forensic voice comparison evidence from the acoustics of filled pauses

This study investigates the evidential value of filled pauses (FPs, i.e. um, uh) as variables in forensic voice comparison. FPs for 60 young male speakers of standard southern British English were analysed. The following acoustic properties were analysed: midpoint frequencies of the first three formants in the vocalic portion; ‘dynamic’ characterisations of formant trajectories (i.e. quadratic polynomial equations fitted to nine measurement points over the entire vowel); vowel duration; and nasal duration for um. Likelihood ratio (LR) scores were computed using the Multivariate Kernel Density formula (MVKD; Aitken and Lucy, 2004) and converted to calibrated log10 LRs (LLRs) using logistic-regression (Brümmer et al., 2007). System validity was assessed using both equal error rate (EER) and the log LR cost function (Cllr; Brümmer and du Preez, 2006). The system with the best performance combines dynamic measurements of all three formants with vowel and nasal duration for um, achieving an EER of 4.08% and Cllr of 0.12. In terms of general patterns, um consistently outperformed uh. For um, the formant dynamic systems generated better validity than those based on midpoints, presumably reflecting the additional degree of formant movement in um caused by the transition from vowel to nasal. By contrast, midpoints outperformed dynamics for the more monophthongal uh. Further, the addition of duration (vowel or vowel and nasal) consistently improved system performance. The study supports the view that FPs have excellent potential as variables in forensic voice comparison cases

Strength of forensic voice comparison evidence from the acoustics of filled pauses

This study investigates the evidential value of filled pauses (FPs, i.e. um, uh) as variables in forensic voice comparison. FPs for 60 young male speakers of standard southern British English were analysed. The following acoustic properties were analysed: midpoint frequencies of the first three formants in the vocalic portion; ‘dynamic’ characterisations of formant trajectories (i.e. quadratic polynomial equations fitted to nine measurement points over the entire vowel); vowel duration; and nasal duration for um. Likelihood ratio (LR) scores were computed using the Multivariate Kernel Density formula (MVKD; Aitken and Lucy, 2004) and converted to calibrated log10 LRs (LLRs) using logistic-regression (Brümmer et al., 2007). System validity was assessed using both equal error rate (EER) and the log LR cost function (Cllr; Brümmer and du Preez, 2006). The system with the best performance combines dynamic measurements of all three formants with vowel and nasal duration for um, achieving an EER of 4.08% and Cllr of 0.12. In terms of general patterns, um consistently outperformed uh. For um, the formant dynamic systems generated better validity than those based on midpoints, presumably reflecting the additional degree of formant movement in um caused by the transition from vowel to nasal. By contrast, midpoints outperformed dynamics for the more monophthongal uh. Further, the addition of duration (vowel or vowel and nasal) consistently improved system performance. The study supports the view that FPs have excellent potential as variables in forensic voice comparison cases

Analysis of Large-Scale SVM Training Algorithms for Language and Speaker Recognition

This paper compares a set of large scale support vector machine (SVM) training algorithms for language and speaker recognition tasks.We analyze five approaches for training phonetic and acoustic SVM models for language recognition. We compare the performance of these approaches as a function of the training time required by each of them to reach convergence, and we discuss their scalability towards large corpora. Two of these algorithms can be used in speaker recognition to train a SVM that classifies pairs of utterances as either belonging to the same speaker or to two different speakers. Our results show that the accuracy of these algorithms is asymptotically equivalent, but they have different behavior with respect to the time required to converge. Some of these algorithms not only scale linearly with the training set size, but are also able to give their best results after just a few iterations. State-of-the-art performance has been obtained in the female subset of the NIST 2010 Speaker Recognition Evaluation extended core test using a single SVM syste

A detection-based pattern recognition framework and its applications

The objective of this dissertation is to present a detection-based pattern recognition framework and demonstrate its applications in automatic speech recognition and broadcast news video story segmentation. Inspired by the studies of modern cognitive psychology and real-world pattern recognition systems, a detection-based pattern recognition framework is proposed to provide an alternative solution for some complicated pattern recognition problems. The primitive features are first detected and the task-specific knowledge hierarchy is constructed level by level; then a variety of heterogeneous information sources are combined together and the high-level context is incorporated as additional information at certain stages. A detection-based framework is a â divide-and-conquerâ design paradigm for pattern recognition problems, which will decompose a conceptually difficult problem into many elementary sub-problems that can be handled directly and reliably. Some information fusion strategies will be employed to integrate the evidence from a lower level to form the evidence at a higher level. Such a fusion procedure continues until reaching the top level. Generally, a detection-based framework has many advantages: (1) more flexibility in both detector design and fusion strategies, as these two parts can be optimized separately; (2) parallel and distributed computational components in primitive feature detection. In such a component-based framework, any primitive component can be replaced by a new one while other components remain unchanged; (3) incremental information integration; (4) high level context information as additional information sources, which can be combined with bottom-up processing at any stage. This dissertation presents the basic principles, criteria, and techniques for detector design and hypothesis verification based on the statistical detection and decision theory. In addition, evidence fusion strategies were investigated in this dissertation. Several novel detection algorithms and evidence fusion methods were proposed and their effectiveness was justified in automatic speech recognition and broadcast news video segmentation system. We believe such a detection-based framework can be employed in more applications in the future.Ph.D.Committee Chair: Lee, Chin-Hui; Committee Member: Clements, Mark; Committee Member: Ghovanloo, Maysam; Committee Member: Romberg, Justin; Committee Member: Yuan, Min

Statistical Methods for Signal Processing with Application to Automatic Accent Recognition

The problem of classification of people based on their phonetic features of accents is posted. This thesis intends to construct an automatic accent recognition machine that can accomplish this classification task with a decent accuracy. The machine consists of two crucial steps, feature extraction and pattern recognition. In the thesis, we review and explore multiple techniques of both steps in great detail. Specifically, in terms of feature extraction, we explore the techniques of principal component analysis and cepstral analysis, and in terms of pattern recognition, we explore the algorithms of discriminant function, support vector machine, and k-nearest neighbors. Since signal data usually exhibit the feature of High Dimension Low Sample Size, it is crucial in the automatic accent recognition task to reduce the dimensionality. Two studies are constructed in which speech signals are collected and a binary classification of American English accent and non-American English accent is performed. In the first study, a total of 330 speech signals, without the disturbance of noise, of an average dimensionality of 44050 are classified into two categories. In the time domain, the dimensionality is reduced to 250 using principal component analysis. Although the in-sample prediction shows an optimistic accuracy of over 90%, the out-of-sample prediction accuracy using cross-validation is as low as 60%. Alternatively, a feature extraction technique in the frequency domain, cepstral analysis, is implemented instead of principal component analysis, by which a special type of feature called mel-frequency cepstral coefficients is extracted and the dimensionality is reduced to some values between 12 and 39. The out-of-sample prediction accuracy can be as high as around 95%. Although cepstral analysis demonstrates itself as a powerful tool in accent recognition, through asecond study we further show that it may quickly fail when there is evident amount of noise in the signal. The prediction performance is reduced to 80% or lower, depending on the amplitude of the noise and the length of the signals