Acoustic Approaches to Gender and Accent Identification

There has been considerable research on the problems of speaker and language recognition from samples of speech. A less researched problem is that of accent recognition. Although this is a similar problem to language identification, di�erent accents of a language exhibit more fine-grained di�erences between classes than languages. This presents a tougher problem for traditional classification techniques. In this thesis, we propose and evaluate a number of techniques for gender and accent classification. These techniques are novel modifications and extensions to state of the art algorithms, and they result in enhanced performance on gender and accent recognition. The first part of the thesis focuses on the problem of gender identification, and presents a technique that gives improved performance in situations where training and test conditions are mismatched. The bulk of this thesis is concerned with the application of the i-Vector technique to accent identification, which is the most successful approach to acoustic classification to have emerged in recent years. We show that it is possible to achieve high accuracy accent identification without reliance on transcriptions and without utilising phoneme recognition algorithms. The thesis describes various stages in the development of i-Vector based accent classification that improve the standard approaches usually applied for speaker or language identification, which are insu�cient. We demonstrate that very good accent identification performance is possible with acoustic methods by considering di�erent i-Vector projections, frontend parameters, i-Vector configuration parameters, and an optimised fusion of the resulting i-Vector classifiers we can obtain from the same data. We claim to have achieved the best accent identification performance on the test corpus for acoustic methods, with up to 90% identification rate. This performance is even better than previously reported acoustic-phonotactic based systems on the same corpus, and is very close to performance obtained via transcription based accent identification. Finally, we demonstrate that the utilization of our techniques for speech recognition purposes leads to considerably lower word error rates. Keywords: Accent Identification, Gender Identification, Speaker Identification, Gaussian Mixture Model, Support Vector Machine, i-Vector, Factor Analysis, Feature Extraction, British English, Prosody, Speech Recognition

Spoken content retrieval: A survey of techniques and technologies

Speech media, that is, digital audio and video containing spoken content, has blossomed in recent years. Large collections are accruing on the Internet as well as in private and enterprise settings. This growth has motivated extensive research on techniques and technologies that facilitate reliable indexing and retrieval. Spoken content retrieval (SCR) requires the combination of audio and speech processing technologies with methods from information retrieval (IR). SCR research initially investigated planned speech structured in document-like units, but has subsequently shifted focus to more informal spoken content produced spontaneously, outside of the studio and in conversational settings. This survey provides an overview of the field of SCR encompassing component technologies, the relationship of SCR to text IR and automatic speech recognition and user interaction issues. It is aimed at researchers with backgrounds in speech technology or IR who are seeking deeper insight on how these fields are integrated to support research and development, thus addressing the core challenges of SCR

Automatic speech recognition: from study to practice

Today, automatic speech recognition (ASR) is widely used for different purposes such as robotics, multimedia, medical and industrial application. Although many researches have been performed in this field in the past decades, there is still a lot of room to work. In order to start working in this area, complete knowledge of ASR systems as well as their weak points and problems is inevitable. Besides that, practical experience improves the theoretical knowledge understanding in a reliable way. Regarding to these facts, in this master thesis, we have first reviewed the principal structure of the standard HMM-based ASR systems from technical point of view. This includes, feature extraction, acoustic modeling, language modeling and decoding. Then, the most significant challenging points in ASR systems is discussed. These challenging points address different internal components characteristics or external agents which affect the ASR systems performance. Furthermore, we have implemented a Spanish language recognizer using HTK toolkit. Finally, two open research lines according to the studies of different sources in the field of ASR has been suggested for future work

Confusion modelling for lip-reading

Lip-reading is mostly used as a means of communication by people with hearing di�fficulties. Recent work has explored the automation of this process, with the aim of building a speech recognition system entirely driven by lip movements. However, this work has so far produced poor results because of factors such as high variability of speaker features, diffi�culties in mapping from visual features to speech sounds, and high co-articulation of visual features. The motivation for the work in this thesis is inspired by previous work in dysarthric speech recognition [Morales, 2009]. Dysathric speakers have poor control over their articulators, often leading to a reduced phonemic repertoire. The premise of this thesis is that recognition of the visual speech signal is a similar problem to recog- nition of dysarthric speech, in that some information about the speech signal has been lost in both cases, and this brings about a systematic pattern of errors in the decoded output. This work attempts to exploit the systematic nature of these errors by modelling them in the framework of a weighted finite-state transducer cascade. Results indicate that the technique can achieve slightly lower error rates than the conventional approach. In addition, it explores some interesting more general questions for automated lip-reading

Modeling DNN as human learner

In previous experiments, human listeners demonstrated that they had the ability to adapt to unheard, ambiguous phonemes after some initial, relatively short exposures. At the same time, previous work in the speech community has shown that pre-trained deep neural network-based (DNN) ASR systems, like humans, also have the ability to adapt to unseen, ambiguous phonemes after retuning their parameters on a relatively small set. In the first part of this thesis, the time-course of phoneme category adaptation in a DNN is investigated in more detail. By retuning the DNNs with more and more tokens with ambiguous sounds and comparing classification accuracy of the ambiguous phonemes in a held-out test across the time-course, we found out that DNNs, like human listeners, also demonstrated fast adaptation: the accuracy curves were step-like in almost all cases, showing very little adaptation after seeing only one (out of ten) training bins. However, unlike our experimental setup mentioned above, in a typical lexically guided perceptual learning experiment, listeners are trained with individual words instead of individual phones, and thus to truly model such a scenario, we would require a model that could take the context of a whole utterance into account. Traditional speech recognition systems accomplish this through the use of hidden Markov models (HMM) and WFST decoding. In recent years, bidirectional long short-term memory (Bi-LSTM) trained under connectionist temporal classification (CTC) criterion has also attracted much attention. In the second part of this thesis, previous experiments on ambiguous phoneme recognition were carried out again on a new Bi-LSTM model, and phonetic transcriptions of words ending with ambiguous phonemes were used as training targets, instead of individual sounds that consisted of a single phoneme. We found out that despite the vastly different architecture, the new model showed highly similar behavior in terms of classification rate over the time course of incremental retuning. This indicated that ambiguous phonemes in a continuous context could also be quickly adapted by neural network-based models. In the last part of this thesis, our pre-trained Dutch Bi-LSTM from the previous part was treated as a Dutch second language learner and was asked to transcribe English utterances in a self-adaptation scheme. In other words, we used the Dutch model to generate phonetic transcriptions directly and retune the model on the transcriptions it generated, although ground truth transcriptions were used to choose a subset of all self-labeled transcriptions. Self-adaptation is of interest as a model of human second language learning, but also has great practical engineering value, e.g., it could be used to adapt speech recognition to a lowr-resource language. We investigated two ways to improve the adaptation scheme, with the first being multi-task learning with articulatory feature detection during training the model on Dutch and self-labeled adaptation, and the second being first letting the model adapt to isolated short words before feeding it with longer utterances.Ope

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

Perceptual compensation for reverberation in human listeners and machines

This thesis explores compensation for reverberation in human listeners and machines. Late reverberation is typically understood as a distortion which degrades intelligibility. Recent research, however, shows that late reverberation is not always detrimental to human speech perception. At times, prolonged exposure to reverberation can provide a helpful acoustic context which improves identification of reverberant speech sounds. The physiology underpinning our robustness to reverberation has not yet been elucidated, but is speculated in this thesis to include efferent processes which have previously been shown to improve discrimination of noisy speech. These efferent pathways descend from higher auditory centres, effectively recalibrating the encoding of sound in the cochlea. Moreover, this thesis proposes that efferent-inspired computational models based on psychoacoustic principles may also improve performance for machine listening systems in reverberant environments. A candidate model for perceptual compensation for reverberation is proposed in which efferent suppression derives from the level of reverberation detected in the simulated auditory nerve response. The model simulates human performance in a phoneme-continuum identification task under a range of reverberant conditions, where a synthetically controlled test-word and its surrounding context phrase are independently reverberated. Addressing questions which arose from the model, a series of perceptual experiments used naturally spoken speech materials to investigate aspects of the psychoacoustic mechanism underpinning compensation. These experiments demonstrate a monaural compensation mechanism that is influenced by both the preceding context (which need not be intelligible speech) and by the test-word itself, and which depends on the time-direction of reverberation. Compensation was shown to act rapidly (within a second or so), indicating a monaural mechanism that is likely to be effective in everyday listening. Finally, the implications of these findings for the future development of computational models of auditory perception are considered