Articulatory-WaveNet: Deep Autoregressive Model for Acoustic-to-Articulatory Inversion

Acoustic-to-Articulatory Inversion, the estimation of articulatory kinematics from speech, is an important problem which has received significant attention in recent years. Estimated articulatory movements from such models can be used for many applications, including speech synthesis, automatic speech recognition, and facial kinematics for talking-head animation devices. Knowledge about the position of the articulators can also be extremely useful in speech therapy systems and Computer-Aided Language Learning (CALL) and Computer-Aided Pronunciation Training (CAPT) systems for second language learners. Acoustic-to-Articulatory Inversion is a challenging problem due to the complexity of articulation patterns and significant inter-speaker differences. This is even more challenging when applied to non-native speakers without any kinematic training data. This dissertation attempts to address these problems through the development of up-graded architectures for Articulatory Inversion. The proposed Articulatory-WaveNet architecture is based on a dilated causal convolutional layer structure that improves the Acoustic-to-Articulatory Inversion estimated results for both speaker-dependent and speaker-independent scenarios. The system has been evaluated on the ElectroMagnetic Articulography corpus of Mandarin Accented English (EMA-MAE) corpus, consisting of 39 speakers including both native English speakers and Mandarin accented English speakers. Results show that Articulatory-WaveNet improves the performance of the speaker-dependent and speaker-independent Acoustic-to-Articulatory Inversion systems significantly compared to the previously reported results

Direct Speech Reconstruction From Articulatory Sensor Data by Machine Learning

This paper describes a technique that generates speech acoustics from articulator movements. Our motivation is to help people who can no longer speak following laryngectomy, a procedure that is carried out tens of thousands of times per year in the Western world. Our method for sensing articulator movement, permanent magnetic articulography, relies on small, unobtrusive magnets attached to the lips and tongue. Changes in magnetic field caused by magnet movements are sensed and form the input to a process that is trained to estimate speech acoustics. In the experiments reported here this “Direct Synthesis” technique is developed for normal speakers, with glued-on magnets, allowing us to train with parallel sensor and acoustic data. We describe three machine learning techniques for this task, based on Gaussian mixture models, deep neural networks, and recurrent neural networks (RNNs). We evaluate our techniques with objective acoustic distortion measures and subjective listening tests over spoken sentences read from novels (the CMU Arctic corpus). Our results show that the best performing technique is a bidirectional RNN (BiRNN), which employs both past and future contexts to predict the acoustics from the sensor data. BiRNNs are not suitable for synthesis in real time but fixed-lag RNNs give similar results and, because they only look a little way into the future, overcome this problem. Listening tests show that the speech produced by this method has a natural quality that preserves the identity of the speaker. Furthermore, we obtain up to 92% intelligibility on the challenging CMU Arctic material. To our knowledge, these are the best results obtained for a silent-speech system without a restricted vocabulary and with an unobtrusive device that delivers audio in close to real time. This work promises to lead to a technology that truly will give people whose larynx has been removed their voices back

EMG-to-Speech: Direct Generation of Speech from Facial Electromyographic Signals

The general objective of this work is the design, implementation, improvement and evaluation of a system that uses surface electromyographic (EMG) signals and directly synthesizes an audible speech output: EMG-to-speech

SYNTHESIZING DYSARTHRIC SPEECH USING MULTI-SPEAKER TTS FOR DSYARTHRIC SPEECH RECOGNITION

Dysarthria is a motor speech disorder often characterized by reduced speech intelligibility through slow, uncoordinated control of speech production muscles. Automatic Speech recognition (ASR) systems may help dysarthric talkers communicate more effectively. However, robust dysarthria-specific ASR requires a significant amount of training speech is required, which is not readily available for dysarthric talkers. In this dissertation, we investigate dysarthric speech augmentation and synthesis methods. To better understand differences in prosodic and acoustic characteristics of dysarthric spontaneous speech at varying severity levels, a comparative study between typical and dysarthric speech was conducted. These characteristics are important components for dysarthric speech modeling, synthesis, and augmentation. For augmentation, prosodic transformation and time-feature masking have been proposed. For dysarthric speech synthesis, this dissertation has introduced a modified neural multi-talker TTS by adding a dysarthria severity level coefficient and a pause insertion model to synthesize dysarthric speech for varying severity levels. In addition, we have extended this work by using a label propagation technique to create more meaningful control variables such as a continuous Respiration, Laryngeal and Tongue (RLT) parameter, even for datasets that only provide discrete dysarthria severity level information. This approach increases the controllability of the system, so we are able to generate more dysarthric speech with a broader range. To evaluate their effectiveness for synthesis of training data, dysarthria-specific speech recognition was used. Results show that a DNN-HMM model trained on additional synthetic dysarthric speech achieves WER improvement of 12.2% compared to the baseline, and that the addition of the severity level and pause insertion controls decrease WER by 6.5%, showing the effectiveness of adding these parameters. Overall results on the TORGO database demonstrate that using dysarthric synthetic speech to increase the amount of dysarthric-patterned speech for training has a significant impact on the dysarthric ASR systems

The Effect of Speaking Rate on Vowel Variability Based on the Uncontrolled Manifold Approach and Flow-Based Invertible Neural Network Modeling

Variability is intrinsic to human speech production. One approach to understand variability in speech is to decompose it into task-irrelevant (“good”) and task-relevant (“bad”) parts with respect to speech tasks. Based on the uncontrolled manifold (UCM) approach, this dissertation investigates how vowel token-to-token variability in articulation and acoustics can be decomposed into “good” and “bad” parts and how speaking rate changes the pattern of these two from the Haskins IEEE rate comparison database. Furthermore, it is examined whether the “good” part of variability, or flexibility, can be modeled directly from speech data using the flow-based invertible neural networks framework. The application of the UCM analysis and FlowINN modeling method is discussed, particularly focusing on how the “good” part of variability in speech can be useful rather than being disregarded as noise

Linear dynamic models for automatic speech recognition

The majority of automatic speech recognition (ASR) systems rely on hidden Markov models (HMM), in which the output distribution associated with each state is modelled by a mixture of diagonal covariance Gaussians. Dynamic information is typically included by appending time-derivatives to feature vectors. This approach, whilst successful, makes the false assumption of framewise independence of the augmented feature vectors and ignores the spatial correlations in the parametrised speech signal. This dissertation seeks to address these shortcomings by exploring acoustic modelling for ASR with an application of a form of state-space model, the linear dynamic model (LDM). Rather than modelling individual frames of data, LDMs characterize entire segments of speech. An auto-regressive state evolution through a continuous space gives a Markovian model of the underlying dynamics, and spatial correlations between feature dimensions are absorbed into the structure of the observation process. LDMs have been applied to speech recognition before, however a smoothed Gauss-Markov form was used which ignored the potential for subspace modelling. The continuous dynamical state means that information is passed along the length of each segment. Furthermore, if the state is allowed to be continuous across segment boundaries, long range dependencies are built into the system and the assumption of independence of successive segments is loosened. The state provides an explicit model of temporal correlation which sets this approach apart from frame-based and some segment-based models where the ordering of the data is unimportant. The benefits of such a model are examined both within and between segments. LDMs are well suited to modelling smoothly varying, continuous, yet noisy trajectories such as found in measured articulatory data. Using speaker-dependent data from the MOCHA corpus, the performance of systems which model acoustic, articulatory, and combined acoustic-articulatory features are compared. As well as measured articulatory parameters, experiments use the output of neural networks trained to perform an articulatory inversion mapping. The speaker-independent TIMIT corpus provides the basis for larger scale acoustic-only experiments. Classification tasks provide an ideal means to compare modelling choices without the confounding influence of recognition search errors, and are used to explore issues such as choice of state dimension, front-end acoustic parametrization and parameter initialization. Recognition for segment models is typically more computationally expensive than for frame-based models. Unlike frame-level models, it is not always possible to share likelihood calculations for observation sequences which occur within hypothesized segments that have different start and end times. Furthermore, the Viterbi criterion is not necessarily applicable at the frame level. This work introduces a novel approach to decoding for segment models in the form of a stack decoder with A* search. Such a scheme allows flexibility in the choice of acoustic and language models since the Viterbi criterion is not integral to the search, and hypothesis generation is independent of the particular language model. Furthermore, the time-asynchronous ordering of the search means that only likely paths are extended, and so a minimum number of models are evaluated. The decoder is used to give full recognition results for feature-sets derived from the MOCHA and TIMIT corpora. Conventional train/test divisions and choice of language model are used so that results can be directly compared to those in other studies. The decoder is also used to implement Viterbi training, in which model parameters are alternately updated and then used to re-align the training data

Joint Training Methods for Tandem and Hybrid Speech Recognition Systems using Deep Neural Networks

Hidden Markov models (HMMs) have been the mainstream acoustic modelling approach for state-of-the-art automatic speech recognition (ASR) systems over the past few decades. Recently, due to the rapid development of deep learning technologies, deep neural networks (DNNs) have become an essential part of nearly all kinds of ASR approaches. Among HMM-based ASR approaches, DNNs are most commonly used to extract features (tandem system configuration) or to directly produce HMM output probabilities (hybrid system configuration). Although DNN tandem and hybrid systems have been shown to have superior performance to traditional ASR systems without any DNN models, there are still issues with such systems. First, some of the DNN settings, such as the choice of the context-dependent (CD) output targets set and hidden activation functions, are usually determined independently from the DNN training process. Second, different ASR modules are separately optimised based on different criteria following a greedy build strategy. For instance, for tandem systems, the features are often extracted by a DNN trained to classify individual speech frames while acoustic models are built upon such features according to a sequence level criterion. These issues mean that the best performance is not theoretically guaranteed. This thesis focuses on alleviating both issues using joint training methods. In DNN acoustic model joint training, the decision tree HMM state tying approach is extended to cluster DNN-HMM states. Based on this method, an alternative CD-DNN training procedure without relying on any additional system is proposed, which can produce DNN acoustic models comparable in word error rate (WER) with those trained by the conventional procedure. Meanwhile, the most common hidden activation functions, the sigmoid and rectified linear unit (ReLU), are parameterised to enable automatic learning of function forms. Experiments using conversational telephone speech (CTS) Mandarin data result in an average of 3.4% and 2.2% relative character error rate (CER) reduction with sigmoid and ReLU parameterisations. Such parameterised functions can also be applied to speaker adaptation tasks. At the ASR system level, DNN acoustic model and corresponding speaker dependent (SD) input feature transforms are jointly learned through minimum phone error (MPE) training as an example of hybrid system joint training, which outperforms the conventional hybrid system speaker adaptive training (SAT) method. MPE based speaker independent (SI) tandem system joint training is also studied. Experiments on multi-genre broadcast (MGB) English data show that this method gives a reduction in tandem system WER of 11.8% (relative), and the resulting tandem systems are comparable to MPE hybrid systems in both WER and the number of parameters. In addition, all approaches in this thesis have been implemented using the hidden Markov model toolkit (HTK) and the related source code has been or will be made publicly available with either recent or future HTK releases, to increase the reproducibility of the work presented in this thesis.Cambridge International Scholarship, Cambridge Overseas Trust Research funding, EPSRC Natural Speech Technology Project Research funding, DARPA BOLT Program Research funding, iARPA Babel Progra

Reconstruction of intelligible audio speech from visual speech information

The aim of the work conducted in this thesis is to reconstruct audio speech signals using information which can be extracted solely from a visual stream of a speaker's face, with application for surveillance scenarios and silent speech interfaces. Visual speech is limited to that which can be seen of the mouth, lips, teeth, and tongue, where the visual articulators convey considerably less information than in the audio domain, leading to the task being difficult. Accordingly, the emphasis is on the reconstruction of intelligible speech, with less regard given to quality. A speech production model is used to reconstruct audio speech, where methods are presented in this work for generating or estimating the necessary parameters for the model. Three approaches are explored for producing spectral-envelope estimates from visual features as this parameter provides the greatest contribution to speech intelligibility. The first approach uses regression to perform the visual-to-audio mapping, and then two further approaches are explored using vector quantisation techniques and classification models, with long-range temporal information incorporated at the feature and model-level. Excitation information, namely fundamental frequency and aperiodicity, is generated using artificial methods and joint-feature clustering approaches. Evaluations are first performed using mean squared error analyses and objective measures of speech intelligibility to refine the various system configurations, and then subjective listening tests are conducted to determine word-level accuracy, giving real intelligibility scores, of reconstructed speech. The best performing visual-to-audio domain mapping approach, using a clustering-and-classification framework with feature-level temporal encoding, is able to achieve audio-only intelligibility scores of 77 %, and audiovisual intelligibility scores of 84 %, on the GRID dataset. Furthermore, the methods are applied to a larger and more continuous dataset, with less favourable results, but with the belief that extensions to the work presented will yield a further increase in intelligibility