Phonological Knowledge Guided HMM State Mapping for Cross-Lingual Speaker Adaptation

Within the HMM state mapping-based cross-lingual speaker adaptation framework, the minimum Kullback-Leibler divergence criterion has been typically employed to measure the similarity of two average voice state distributions from two respective languages for state mapping construction. Considering that this simple criterion doesn't take any language-specific information into account, we propose a data-driven, phonological knowledge guided approach to strengthen the mapping construction -- state distributions from the two languages are clustered according to broad phonetic categories using decision trees and mapping rules are constructed only within each of the clusters. Objective evaluation of our proposed approach demonstrates reduction of mel-cepstral distortion and that mapping rules derived from a single training speaker generalize to other speakers, with subtle improvement being detected during subjective listening tests

Data-Driven Enhancement of State Mapping-Based Cross-Lingual Speaker Adaptation

The thesis work was motivated by the goal of developing personalized speech-to-speech translation and focused on one of its key component techniques – cross-lingual speaker adaptation for text-to-speech synthesis. A personalized speech-to-speech translator enables a person’s spoken input to be translated into spoken output in another language while maintaining his/her voice identity. Before addressing any technical issues, work in this thesis set out to understand human perception of speaker identity. Listening tests were conducted in order to determine whether people could differentiate between speakers when they spoke different languages. The results demonstrated that differentiating between speakers across languages was an achievable task. However, it was difficult for listeners to differentiate between speakers across both languages and speech types (original recordings versus synthesized samples). The underlying challenge in cross-lingual speaker adaptation is how to apply speaker adaptation techniques when the language of adaptation data is different from that of synthesis models. The main body of the thesis work was devoted to the analysis and improvement of HMM state mapping-based cross-lingual speaker adaptation. Firstly, the effect of unsupervised cross-lingual adaptation was investigated, as it relates to the application scenario of personalized speech-to-speech translation. The comparison of paired supervised and unsupervised systems shows that the performance of unsupervised cross-lingual speaker adaptation is comparable to that of the supervised fashion, even if the average phoneme error rate of the unsupervised systems is around 75%. Then the effect of the language mismatch between synthesis models and adaptation data was investigated. The mismatch is found to transfer undesirable language information from adaptation data to synthesis models, thereby limiting the effectiveness of generating multiple regression class-specific transforms, using larger quantities of adaptation data and estimating adaptation transforms iteratively. Thirdly, in order to tackle the problems caused by the language mismatch, a data-driven adaptation framework using phonological knowledge is proposed. Its basic idea is to group HMM states according to phonological knowledge in a data-driven manner and then to map each state to a phonologically consistent counterpart in a different language. This framework is also applied to regression class tree construction for transform estimation. It is found that the proposed framework alleviates the negative effect of the language mismatch and gives consistent improvement compared to previous state-of-the-art approaches. Finally, a two-layer hierarchical transformation framework is developed, where one layer captures speaker characteristics and the other compensates for the language mismatch. The most appropriate means to construct the hierarchical arrangement of transforms was investigated in an initial study. While early results show some promise, further in-depth investigation is needed to confirm the validity of this hierarchy

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

Intonation Modelling for Speech Synthesis and Emphasis Preservation

Speech-to-speech translation is a framework which recognises speech in an input language, translates it to a target language and synthesises speech in this target language. In such a system, variations in the speech signal which are inherent to natural human speech are lost, as the information goes through the different building blocks of the translation process. The work presented in this thesis addresses aspects of speech synthesis which are lost in traditional speech-to-speech translation approaches. The main research axis of this thesis is the study of prosody for speech synthesis and emphasis preservation. A first investigation of regional accents of spoken French is carried out to understand the sensitivity of native listeners with respect to accented speech synthesis. Listening tests show that standard adaptation methods for speech synthesis are not sufficient for listeners to perceive accentedness. On the other hand, combining adaptation with original prosody allows perception of accents. Addressing the need of a more suitable prosody model, a physiologically plausible intonation model is proposed. Inspired by the command-response model, it has basic components, which can be related to muscle responses to nerve impulses. These components are assumed to be a representation of muscle control of the vocal folds. A motivation for such a model is its theoretical language independence, based on the fact that humans share the same vocal apparatus. An automatic parameter extraction method which integrates a perceptually relevant measure is proposed with the model. This approach is evaluated and compared with the standard command-response model. Two corpora including sentences with emphasised words are presented, in the context of the SIWIS project. The first is a multilingual corpus with speech from multiple speaker; the second is a high quality speech synthesis oriented corpus from a professional speaker. Two broad uses of the model are evaluated. The first shows that it is difficult to predict model parameters; however the second shows that parameters can be transferred in the context of emphasis synthesis. A relation between model parameters and linguistic features such as stress and accent is demonstrated. Similar observations are made between the parameters and emphasis. Following, we investigate the extraction of atoms in emphasised speech and their transfer in neutral speech, which turns out to elicit emphasis perception. Using clustering methods, this is extended to the emphasis of other words, using linguistic context. This approach is validated by listening tests, in the case of English

Automatic Screening of Childhood Speech Sound Disorders and Detection of Associated Pronunciation Errors

Speech disorders in children can affect their fluency and intelligibility. Delay in their diagnosis and treatment increases the risk of social impairment and learning disabilities. With the significant shortage of Speech and Language Pathologists (SLPs), there is an increasing interest in Computer-Aided Speech Therapy tools with automatic detection and diagnosis capability. However, the scarcity and unreliable annotation of disordered child speech corpora along with the high acoustic variations in the child speech data has impeded the development of reliable automatic detection and diagnosis of childhood speech sound disorders. Therefore, this thesis investigates two types of detection systems that can be achieved with minimum dependency on annotated mispronounced speech data. First, a novel approach that adopts paralinguistic features which represent the prosodic, spectral, and voice quality characteristics of the speech was proposed to perform segment- and subject-level classification of Typically Developing (TD) and Speech Sound Disordered (SSD) child speech using a binary Support Vector Machine (SVM) classifier. As paralinguistic features are both language- and content-independent, they can be extracted from an unannotated speech signal. Second, a novel Mispronunciation Detection and Diagnosis (MDD) approach was introduced to detect the pronunciation errors made due to SSDs and provide low-level diagnostic information that can be used in constructing formative feedback and a detailed diagnostic report. Unlike existing MDD methods where detection and diagnosis are performed at the phoneme level, the proposed method achieved MDD at the speech attribute level, namely the manners and places of articulations. The speech attribute features describe the involved articulators and their interactions when making a speech sound allowing a low-level description of the pronunciation error to be provided. Two novel methods to model speech attributes are further proposed in this thesis, a frame-based (phoneme-alignment) method leveraging the Multi-Task Learning (MTL) criterion and training a separate model for each attribute, and an alignment-free jointly-learnt method based on the Connectionist Temporal Classification (CTC) sequence to sequence criterion. The proposed techniques have been evaluated using standard and publicly accessible adult and child speech corpora, while the MDD method has been validated using L2 speech corpora

Speech production knowledge in automatic speech recognition

Although much is known about how speech is produced, and research into speech production has resulted in measured articulatory data, feature systems of different kinds and numerous models, speech production knowledge is almost totally ignored in current mainstream approaches to automatic speech recognition. Representations of speech production allow simple explanations for many phenomena observed in speech which cannot be easily analyzed from either acoustic signal or phonetic transcription alone. In this article, we provide a survey of a growing body of work in which such representations are used to improve automatic speech recognition