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

Statistical identification of articulatory roles in speech production.

The human speech apparatus is a rich source of information and offers many cues in the speech signal due to its biomechanical constraints and physiological interdependencies. Coarticulation, a direct consequence of these speech production factors, is one of the main problems affecting the performance of speech systems. Incorporation of production knowledge could potentially benefit speech recognisers and synthesisers. Hand coded rules and scores derived from the phonological knowledge used by production oriented models of speech are simple and incomplete representations of the complex speech production process. Statistical models built from measurements of speech articulation fail to identify the cause of constraints. There is a need for building explanatory yet descriptive models of articulation for understanding and modelling the effects of coarticulation. This thesis aims at providing compact descriptive models of realistic speech articulation by identifying and capturing the essential characteristics of human articulators using measurements from electro-magnetic articulography. The constraints on articulators during speech production are identified in the form of critical, dependent and redundant roles using entirely statistical and data-driven methods. The critical role captures the maximally constrained target driven behaviour of an articulator. The dependent role models the partial constraints due to physiological interdependencies. The redundant role reflects the unconstrained behaviour of an articulator which is maximally prone to coarticulation. Statistical target models are also obtained as the by-product of the identified roles. The algorithm for identification of articulatory roles (and estimation of respective model distributions) for each phone is presented and the results are critically evaluated. The identified data-driven constraints obtained are compared with the well known and commonly used constraints derived from the IPA (International Phonetic Alphabet). The identified critical roles were not only in agreement with the place and manner descriptions of each phone but also provided a phoneme to phone transformation by capturing language and speaker specific behaviour of articulators. The models trained from the identified constraints fitted better to the phone distributions (40% improvement) . The evaluation of the proposed search procedure with respect to an exhaustive search for identification of roles demonstrated that the proposed approach performs equally well for much less computational load. Articulation models built in the planning stage using sparse yet efficient articulatory representations using standard trajectory generation techniques showed some potential in modelling articulatory behaviour. Plenty of scope exists for further developing models of articulation from the proposed framework

Development of Kinematic Templates for Automatic Pronunciation Assessment Using Acoustic-to-Articulatory Inversion

Computer-aided pronunciation training (CAPT) is a subcategory of computer-aided language learning (CALL) that deals with the correction of mispronunciation during language learning. For a CAPT system to be effective, it must provide useful and informative feedback that is comprehensive, qualitative, quantitative, and corrective. While the majority of modern systems address the first 3 aspects of feedback, most of these systems do not provide corrective feedback. As part of the National Science Foundation (NSF) funded study “RI: Small: Speaker Independent Acoustic-Articulator Inversion for Pronunciation Assessment”, the Marquette Speech and Swallowing Lab and Marquette Speech and Signal Processing Lab are conducting a pilot study on the feasibility of the use of acoustic-to-articulatory inversion for CAPT. In order to evaluate the results of a speaker’s acoustic-to-articulatory inversion to determine pronunciation accuracy, kinematic templates are required. The templates would represent the vowels, consonant clusters, and stress characteristics of a typical American English (AE) speaker in the midsagittal plane. The Marquette University electromagnetic articulography Mandarin-accented English (EMA-MAE) database, which contains acoustic and kinematic speech data for 40 speakers (20 of which are native AE speakers), provides the data used to form the kinematic templates. The objective of this work is the development and implementation of these templates. The data provided in the EMA-MAE database is analyzed in detail, and the information obtained from the analysis is used to develop the kinematic templates. The vowel templates are designed as sets of concentric confidence ellipses, which specify (in the midsagittal plane) the ranges of tongue and lip positions corresponding to correct pronunciation. These ranges were defined using the typical articulator positioning of all English speakers of the EMA-MAE database. The data from these English speakers were also used to model the magnitude, speed history, movement pattern, and duration (MSTD) features of each consonant cluster in the EMA-MAE corpus. Cluster templates were designed as set of average MSTD parameters across English speakers for each cluster. Finally, English stress characteristics were similarly modeled as a set of average magnitude, speed, and duration parameters across English speakers. The kinematic templates developed in this work, while still in early stages, form the groundwork for assessment of features returned by the acoustic-to-articulatory inversion system. This in turn allows for assessment of articulatory inversion as a pronunciation training tool

Back from the future:Nonlinear anticipation in adults and children's speech

Purpose: This study examines the temporal organization of vocalic anticipation in German children from 3 to 7 years of age and adults. The main objective was to test for non-linearprocesses in vocalic anticipation, which may result from the interaction between lingualgestural goalsfor individual vowels, and those for their neighbors over time. Method: The technique of ultrasound imaging was employed to record tongue movement at fivetimepoints throughout short utterances of the form V1#CV2. Vocalic anticipation was examined with Generalized Additive Modeling, an analytical approach allowing forthe estimation of both linear and non-linearinfluences on anticipatoryprocesses. Results: both adults and children exhibit non-linear patterns of vocalic anticipation over time with the degree and extent of vocalic anticipation varying as a function of the individual consonants and vowels assembled. However, noticeable developmental discrepancieswere found with vocalic anticipation being present earlier in children ́sutterances at 3-4-5 years of agein comparison to adults and to some extent 7-year-old children.Conclusions: Anarrowing of speech production organization from large chunks in kindergarten to more contextually-specified organizationsseems to occur fromkindergarten toprimary school toadulthood, although variation in the temporal overlap of lingual gestures for consecutive segments is already present in the youngestcohorts. In adults, non-linear anticipatory patterns over time suggest a strong differentiation between the gestural goals for consecutive segments. In children, this differentiation is not yet mature: vowelsshow greater prominence over time and seem activated more in-phase with those of previous segments relative to adults

Fundamental frequency modelling: an articulatory perspective with target approximation and deep learning

Current statistical parametric speech synthesis (SPSS) approaches typically aim at state/frame-level acoustic modelling, which leads to a problem of frame-by-frame independence. Besides that, whichever learning technique is used, hidden Markov model (HMM), deep neural network (DNN) or recurrent neural network (RNN), the fundamental idea is to set up a direct mapping from linguistic to acoustic features. Although progress is frequently reported, this idea is questionable in terms of biological plausibility. This thesis aims at addressing the above issues by integrating dynamic mechanisms of human speech production as a core component of F0 generation and thus developing a more human-like F0 modelling paradigm. By introducing an articulatory F0 generation model – target approximation (TA) – between text and speech that controls syllable-synchronised F0 generation, contextual F0 variations are processed in two separate yet integrated stages: linguistic to motor, and motor to acoustic. With the goal of demonstrating that human speech movement can be considered as a dynamic process of target approximation and that the TA model is a valid F0 generation model to be used at the motor-to-acoustic stage, a TA-based pitch control experiment is conducted first to simulate the subtle human behaviour of online compensation for pitch-shifted auditory feedback. Then, the TA parameters are collectively controlled by linguistic features via a deep or recurrent neural network (DNN/RNN) at the linguistic-to-motor stage. We trained the systems on a Mandarin Chinese dataset consisting of both statements and questions. The TA-based systems generally outperformed the baseline systems in both objective and subjective evaluations. Furthermore, the amount of required linguistic features were reduced first to syllable level only (with DNN) and then with all positional information removed (with RNN). Fewer linguistic features as input with limited number of TA parameters as output led to less training data and lower model complexity, which in turn led to more efficient training and faster synthesis

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

Context-aware speech synthesis: A human-inspired model for monitoring and adapting synthetic speech

The aim of this PhD thesis is to illustrate the development a computational model for speech synthesis, which mimics the behaviour of human speaker when they adapt their production to their communicative conditions. The PhD project was motivated by the observed differences between state-of-the- art synthesiser’s speech and human production. In particular, synthesiser outcome does not exhibit any adaptation to communicative context such as environmental disturbances, listener’s needs, or speech content meanings, as the human speech does. No evaluation is performed by standard synthesisers to check whether their production is suitable for the communication requirements. Inspired by Lindblom's Hyper and Hypo articulation theory (H&H) theory of speech production, the computational model of Hyper and Hypo articulation theory (C2H) is proposed. This novel computational model for automatic speech production is designed to monitor its outcome and to be able to control the effort involved in the synthetic speech generation. Speech transformations are based on the hypothesis that low-effort attractors for a human speech production system can be identified. Such acoustic configurations are close to minimum possible effort that a speaker can make in speech production. The interpolation/extrapolation along the key dimension of hypo/hyper-articulation can be motivated by energetic considerations of phonetic contrast. The complete reactive speech synthesis is enabled by adding a negative perception feedback loop to the speech production chain in order to constantly assess the communicative effectiveness of the proposed adaptation. The distance to the original communicative intents is the control signal that drives the speech transformations. A hidden Markov model (HMM)-based speech synthesiser along with the continuous adaptation of its statistical models is used to implement the C2H model. A standard version of the synthesis software does not allow for transformations of speech during the parameter generation. Therefore, the generation algorithm of one the most well-known speech synthesis frameworks, HMM/DNN-based speech synthesis framework (HTS), is modified. The short-time implementation of speech intelligibility index (SII), named extended speech intelligibility index (eSII), is also chosen as the main perception measure in the feedback loop to control the transformation. The effectiveness of the proposed model is tested by performing acoustic analysis, objective, and subjective evaluations. A key assessment is to measure the control of the speech clarity in noisy condition, and the similarities between the emerging modifications and human behaviour. Two objective scoring methods are used to assess the speech intelligibility of the implemented system: the speech intelligibility index (SII) and the index based upon the Dau measure (Dau). Results indicate that the intelligibility of C2H-generated speech can be continuously controlled. The effectiveness of reactive speech synthesis and of the phonetic contrast motivated transforms is confirmed by the acoustic and objective results. More precisely, in the maximum-strength hyper-articulation transformations, the improvement with respect to non-adapted speech is above 10% for all intelligibility indices and tested noise conditions

Models and analysis of vocal emissions for biomedical applications

This book of Proceedings collects the papers presented at the 3rd International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications, MAVEBA 2003, held 10-12 December 2003, Firenze, Italy. The workshop is organised every two years, and aims to stimulate contacts between specialists active in research and industrial developments, in the area of voice analysis for biomedical applications. The scope of the Workshop includes all aspects of voice modelling and analysis, ranging from fundamental research to all kinds of biomedical applications and related established and advanced technologies

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