Estimating underlying articulatory targets of Thai vowels by using deep learning based on generating synthetic samples from a 3D vocal tract model and data augmentation

Representation learning is one of the fundamental issues in modeling articulatory-based speech synthesis using target-driven models. This paper proposes a computational strategy for learning underlying articulatory targets from a 3D articulatory speech synthesis model using a bi-directional long short-term memory recurrent neural network based on a small set of representative seed samples. From a seeding set, a larger training set was generated that provided richer contextual variations for the model to learn. The deep learning model for acoustic-to-target mapping was then trained to model the inverse relation of the articulation process. This method allows the trained model to map the given acoustic data onto the articulatory target parameters which can then be used to identify the distribution based on linguistic contexts. The model was evaluated based on its effectiveness in mapping acoustics to articulation, and the perceptual accuracy of speech reproduced from the estimated articulation. The results indicate that the model can accurately imitate speech with a high degree of phonemic precision

Cortical encoding and decoding models of speech production

To speak is to dynamically orchestrate the movements of the articulators (jaw, tongue, lips, and larynx), which in turn generate speech sounds. It is an amazing mental and motor feat that is controlled by the brain and is fundamental for communication. Technology that could translate brain signals into speech would be transformative for people who are unable to communicate as a result of neurological impairments. This work first investigates how articulator movements that underlie natural speech production are represented in the brain. Building upon this, this work also presents a neural decoder that can synthesize audible speech from brain signals. Data to support these results were from direct cortical recordings of the human sensorimotor cortex while participants spoke natural sentences. Neural activity at individual electrodes encoded a diversity of articulatory kinematic trajectories (AKTs), each revealing coordinated articulator movements towards specific vocal tract shapes. The neural decoder was designed to leverage the kinematic trajectories encoded in the sensorimotor cortex which enhanced performance even with limited data. In closed vocabulary tests, listeners could readily identify and transcribe speech synthesized from cortical activity. These findings advance the clinical viability of using speech neuroprosthetic technology to restore spoken communication

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

Articulatory Copy Synthesis Based on the Speech Synthesizer VocalTractLab

Articulatory copy synthesis (ACS), a subarea of speech inversion, refers to the reproduction of natural utterances and involves both the physiological articulatory processes and their corresponding acoustic results. This thesis proposes two novel methods for the ACS of human speech using the articulatory speech synthesizer VocalTractLab (VTL) to address or mitigate the existing problems of speech inversion, such as non-unique mapping, acoustic variation among different speakers, and the time-consuming nature of the process. The first method involved finding appropriate VTL gestural scores for given natural utterances using a genetic algorithm. It consisted of two steps: gestural score initialization and optimization. In the first step, gestural scores were initialized using the given acoustic signals with speech recognition, grapheme-to-phoneme (G2P), and a VTL rule-based method for converting phoneme sequences to gestural scores. In the second step, the initial gestural scores were optimized by a genetic algorithm via an analysis-by-synthesis (ABS) procedure that sought to minimize the cosine distance between the acoustic features of the synthetic and natural utterances. The articulatory parameters were also regularized during the optimization process to restrict them to reasonable values. The second method was based on long short-term memory (LSTM) and convolutional neural networks, which were responsible for capturing the temporal dependence and the spatial structure of the acoustic features, respectively. The neural network regression models were trained, which used acoustic features as inputs and produced articulatory trajectories as outputs. In addition, to cover as much of the articulatory and acoustic space as possible, the training samples were augmented by manipulating the phonation type, speaking effort, and the vocal tract length of the synthetic utterances. Furthermore, two regularization methods were proposed: one based on the smoothness loss of articulatory trajectories and another based on the acoustic loss between original and predicted acoustic features. The best-performing genetic algorithms and convolutional LSTM systems (evaluated in terms of the difference between the estimated and reference VTL articulatory parameters) obtained average correlation coefficients of 0.985 and 0.983 for speaker-dependent utterances, respectively, and their reproduced speech achieved recognition accuracies of 86.25% and 64.69% for speaker-independent utterances of German words, respectively. When applied to German sentence utterances, as well as English and Mandarin Chinese word utterances, the neural network based ACS systems achieved recognition accuracies of 73.88%, 52.92%, and 52.41%, respectively. The results showed that both of these methods not only reproduced the articulatory processes but also reproduced the acoustic signals of reference utterances. Moreover, the regularization methods led to more physiologically plausible articulatory processes and made the estimated articulatory trajectories be more articulatorily preferred by VTL, thus reproducing more natural and intelligible speech. This study also found that the convolutional layers, when used in conjunction with batch normalization layers, automatically learned more distinctive features from log power spectrograms. Furthermore, the neural network based ACS systems trained using German data could be generalized to the utterances of other languages

Articulatory representations to address acoustic variability in speech

The past decade has seen phenomenal improvement in the performance of Automatic Speech Recognition (ASR) systems. In spite of this vast improvement in performance, the state-of-the-art still lags significantly behind human speech recognition. Even though certain systems claim super-human performance, this performance often is sub-par across domains and across datasets. This gap is predominantly due to the lack of robustness against speech variability. Even clean speech is extremely variable due to a large number of factors such as voice characteristics, speaking style, speaking rate, accents, casualness, emotions and more. The goal of this thesis is to investigate the variability of speech from the perspective of speech production, put forth robust articulatory features to address this variability, and to incorporate these features in state-of-the-art ASR systems in the best way possible. ASR systems model speech as a sequence of distinctive phone units like beads on a string. Although phonemes are distinctive units in the cognitive domain, their physical realizations are extremely varied due to coarticulation and lenition which are commonly observed in conversational speech. The traditional approaches deal with this issue by performing di-, tri- or quin-phone based acoustic modeling but are insufficient to model longer contextual dependencies. Articulatory phonology analyzes speech as a constellation of coordinated articulatory gestures performed by the articulators in the vocal tract (lips, tongue tip, tongue body, jaw, glottis and velum). In this framework, acoustic variability is explained by the temporal overlap of gestures and their reduction in space. In order to analyze speech in terms of articulatory gestures, the gestures need to be estimated from the speech signal. The first part of the thesis focuses on a speaker independent acoustic-to-articulatory inversion system that was developed to estimate vocal tract constriction variables (TVs) from speech. The mapping from acoustics to TVs was learned from the multi-speaker X-ray Microbeam (XRMB) articulatory dataset. Constriction regions from TV trajectories were defined as articulatory gestures using articulatory kinematics. The speech inversion system combined with the TV kinematics based gesture annotation provided a system to estimate articulatory gestures from speech. The second part of this thesis deals with the analysis of the articulatory trajectories under different types of variability such as multiple speakers, speaking rate, and accents. It was observed that speaker variation degraded the performance of the speech inversion system. A Vocal Tract Length Normalization (VTLN) based speaker normalization technique was therefore developed to address the speaker variability in the acoustic and articulatory domains. The performance of speech inversion systems was analyzed on an articulatory dataset containing speaking rate variations to assess if the model was able to reliably predict the TVs in challenging coarticulatory scenarios. The performance of the speech inversion system was analyzed in cross accent and cross language scenarios through experiments on a Dutch and British English articulatory dataset. These experiments provide a quantitative measure of the robustness of the speech inversion systems to different speech variability. The final part of this thesis deals with the incorporation of articulatory features in state-of-the-art medium vocabulary ASR systems. A hybrid convolutional neural network (CNN) architecture was developed to fuse the acoustic and articulatory feature streams in an ASR system. ASR experiments were performed on the Wall Street Journal (WSJ) corpus. Several articulatory feature combinations were explored to determine the best feature combination. Cross-corpus evaluations were carried out to evaluate the WSJ trained ASR system on the TIMIT and another dataset containing speaking rate variability. Results showed that combining articulatory features with acoustic features through the hybrid CNN improved the performance of the ASR system in matched and mismatched evaluation conditions. The findings based on this dissertation indicate that articulatory representations extracted from acoustics can be used to address acoustic variability in speech observed due to speakers, accents, and speaking rates and further be used to improve the performance of Automatic Speech Recognition systems

Registration and statistical analysis of the tongue shape during speech production

This thesis analyzes the human tongue shape during speech production. First, a semi-supervised approach is derived for estimating the tongue shape from volumetric magnetic resonance imaging data of the human vocal tract. Results of this extraction are used to derive parametric tongue models. Next, a framework is presented for registering sparse motion capture data of the tongue by means of such a model. This method allows to generate full three-dimensional animations of the tongue. Finally, a multimodal and statistical text-to-speech system is developed that is able to synthesize audio and synchronized tongue motion from text.Diese Dissertation beschäftigt sich mit der Analyse der menschlichen Zungenform während der Sprachproduktion. Zunächst wird ein semi-überwachtes Verfahren vorgestellt, mit dessen Hilfe sich Zungenformen von volumetrischen Magnetresonanztomographie- Aufnahmen des menschlichen Vokaltrakts schätzen lassen. Die Ergebnisse dieses Extraktionsverfahrens werden genutzt, um ein parametrisches Zungenmodell zu konstruieren. Danach wird eine Methode hergeleitet, die ein solches Modell nutzt, um spärliche Bewegungsaufnahmen der Zunge zu registrieren. Dieser Ansatz erlaubt es, dreidimensionale Animationen der Zunge zu erstellen. Zuletzt wird ein multimodales und statistisches Text-to-Speech-System entwickelt, das in der Lage ist, Audio und die dazu synchrone Zungenbewegung zu synthetisieren.German Research Foundatio

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

Audio Data Augmentation for Acoustic-to-articulatory Speech Inversion using Bidirectional Gated RNNs

Data augmentation has proven to be a promising prospect in improving the performance of deep learning models by adding variability to training data. In previous work with developing a noise robust acoustic-to-articulatory speech inversion system, we have shown the importance of noise augmentation to improve the performance of speech inversion in noisy speech. In this work, we compare and contrast different ways of doing data augmentation and show how this technique improves the performance of articulatory speech inversion not only on noisy speech, but also on clean speech data. We also propose a Bidirectional Gated Recurrent Neural Network as the speech inversion system instead of the previously used feed forward neural network. The inversion system uses mel-frequency cepstral coefficients (MFCCs) as the input acoustic features and six vocal tract-variables (TVs) as the output articulatory features. The Performance of the system was measured by computing the correlation between estimated and actual TVs on the U. Wisc. X-ray Microbeam database. The proposed speech inversion system shows a 5% relative improvement in correlation over the baseline noise robust system for clean speech data. The pre-trained model, when adapted to each unseen speaker in the test set, improves the average correlation by another 6%.Comment: EUSIPCO 202