Articulatory and bottleneck features for speaker-independent ASR of dysarthric speech

The rapid population aging has stimulated the development of assistive devices that provide personalized medical support to the needies suffering from various etiologies. One prominent clinical application is a computer-assisted speech training system which enables personalized speech therapy to patients impaired by communicative disorders in the patient's home environment. Such a system relies on the robust automatic speech recognition (ASR) technology to be able to provide accurate articulation feedback. With the long-term aim of developing off-the-shelf ASR systems that can be incorporated in clinical context without prior speaker information, we compare the ASR performance of speaker-independent bottleneck and articulatory features on dysarthric speech used in conjunction with dedicated neural network-based acoustic models that have been shown to be robust against spectrotemporal deviations. We report ASR performance of these systems on two dysarthric speech datasets of different characteristics to quantify the achieved performance gains. Despite the remaining performance gap between the dysarthric and normal speech, significant improvements have been reported on both datasets using speaker-independent ASR architectures.Comment: to appear in Computer Speech & Language - https://doi.org/10.1016/j.csl.2019.05.002 - arXiv admin note: substantial text overlap with arXiv:1807.1094

ARTICULATORY INFORMATION FOR ROBUST SPEECH RECOGNITION

Current Automatic Speech Recognition (ASR) systems fail to perform nearly as good as human speech recognition performance due to their lack of robustness against speech variability and noise contamination. The goal of this dissertation is to investigate these critical robustness issues, put forth different ways to address them and finally present an ASR architecture based upon these robustness criteria. Acoustic variations adversely affect the performance of current phone-based ASR systems, in which speech is modeled as `beads-on-a-string', where the beads are the individual phone units. While phone units are distinctive in cognitive domain, they are varying in the physical domain and their variation occurs due to a combination of factors including speech style, speaking rate etc.; a phenomenon commonly known as `coarticulation'. Traditional ASR systems address such coarticulatory variations by using contextualized phone-units such as triphones. Articulatory phonology accounts for coarticulatory variations by modeling speech as a constellation of constricting actions known as articulatory gestures. In such a framework, speech variations such as coarticulation and lenition are accounted for by gestural overlap in time and gestural reduction in space. To realize a gesture-based ASR system, articulatory gestures have to be inferred from the acoustic signal. At the initial stage of this research an initial study was performed using synthetically generated speech to obtain a proof-of-concept that articulatory gestures can indeed be recognized from the speech signal. It was observed that having vocal tract constriction trajectories (TVs) as intermediate representation facilitated the gesture recognition task from the speech signal. Presently no natural speech database contains articulatory gesture annotation; hence an automated iterative time-warping architecture is proposed that can annotate any natural speech database with articulatory gestures and TVs. Two natural speech databases: X-ray microbeam and Aurora-2 were annotated, where the former was used to train a TV-estimator and the latter was used to train a Dynamic Bayesian Network (DBN) based ASR architecture. The DBN architecture used two sets of observation: (a) acoustic features in the form of mel-frequency cepstral coefficients (MFCCs) and (b) TVs (estimated from the acoustic speech signal). In this setup the articulatory gestures were modeled as hidden random variables, hence eliminating the necessity for explicit gesture recognition. Word recognition results using the DBN architecture indicate that articulatory representations not only can help to account for coarticulatory variations but can also significantly improve the noise robustness of ASR system

Improving Speech Inversion Through Self-Supervised Embeddings and Enhanced Tract Variables

The performance of deep learning models depends significantly on their capacity to encode input features efficiently and decode them into meaningful outputs. Better input and output representation has the potential to boost models' performance and generalization. In the context of acoustic-to-articulatory speech inversion (SI) systems, we study the impact of utilizing speech representations acquired via self-supervised learning (SSL) models, such as HuBERT compared to conventional acoustic features. Additionally, we investigate the incorporation of novel tract variables (TVs) through an improved geometric transformation model. By combining these two approaches, we improve the Pearson product-moment correlation (PPMC) scores which evaluate the accuracy of TV estimation of the SI system from 0.7452 to 0.8141, a 6.9% increase. Our findings underscore the profound influence of rich feature representations from SSL models and improved geometric transformations with target TVs on the enhanced functionality of SI systems

Automatic English phoneme recognition from articulatory data generated by EPG systems with grid and anatomical layout of contact sensors

The aim of the study was to conduct automatic phoneme identification from articulatory data that accompanied the production of these phonemes in continuous speech. The articulatory data were obtained from 2 electropalatographic systems, Palatometer by Complete Speech and Linguagraph by Rose-Medical. Palatometer was used with the artificial palate containing 124 contact sensors in a grid layout, including 2 sensors monitoring the lip contact. The palate included a vacuum-thermoformed flexible printed circuit. Linguagraph was used with the acrylic artificial palate designed and developed for the purpose of this study, containing 62 electrodes in anatomical layout. Palatometer was used by one native of General American and Linguagraph by one native of General British, each reading 140 phonetically balanced sentences that included Harvard Sentences and TIMIT prompts. The EPG data were parametrised into dimensionality reduction indexes, which were analysed by means of linear discriminant analysis and a probabilistic neural network. The results of classifications are discussed.National Science Centre (grant no. 2013/11/B/HS2/03151

An interactive speech training system with virtual reality articulation for Mandarin-speaking hearing impaired children

The present project involved the development of a novel interactive speech training system based on virtual reality articulation and examination of the efficacy of the system for hearing impaired (HI) children. Twenty meaningful Mandarin words were presented to the HI children via a 3-D talking head during articulation training. Electromagnetic Articulography (EMA) and graphic transform technology were used to depict movements of various articulators. In addition, speech corpuses were organized in listening and speaking training modules of the system to help improve language skills of the HI children. Accuracy of virtual reality articulatory movement was evaluated through a series of experiments. Finally, a pilot test was performed to train two HI children using the system. Preliminary results showed improvement in speech production by the HI children, and the system was recognized as acceptable and interesting for children. It can be concluded that the training system is effective and valid in articulation training for HI children. © 2013 IEEE.published_or_final_versio

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

The magnetic resonance imaging subset of the mngu0 articulatory corpus

Author version contains correctly encoded (Unicode) fonts and attached multimedia content.International audienceThis paper announces the availability of the magnetic resonance imaging (MRI) subset of the mngu0 corpus, a collection of articulatory speech data from one speaker containing different modalities. This subset comprises volumetric MRI scans of the speaker's vocal tract during sustained production of vowels and consonants, as well as dynamic mid-sagittal scans of repetitive consonant-vowel (CV) syllable production. For reference, high-quality acoustic recordings of the speech material are also available. The raw data are made freely available for research purposes

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