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

Real-time decoding of question-and-answer speech dialogue using human cortical activity.

Natural communication often occurs in dialogue, differentially engaging auditory and sensorimotor brain regions during listening and speaking. However, previous attempts to decode speech directly from the human brain typically consider listening or speaking tasks in isolation. Here, human participants listened to questions and responded aloud with answers while we used high-density electrocorticography (ECoG) recordings to detect when they heard or said an utterance and to then decode the utterance's identity. Because certain answers were only plausible responses to certain questions, we could dynamically update the prior probabilities of each answer using the decoded question likelihoods as context. We decode produced and perceived utterances with accuracy rates as high as 61% and 76%, respectively (chance is 7% and 20%). Contextual integration of decoded question likelihoods significantly improves answer decoding. These results demonstrate real-time decoding of speech in an interactive, conversational setting, which has important implications for patients who are unable to communicate

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 Challenge of Spoken Language Systems: Research Directions for the Nineties

A spoken language system combines speech recognition, natural language processing and human interface technology. It functions by recognizing the person's words, interpreting the sequence of words to obtain a meaning in terms of the application, and providing an appropriate response back to the user. Potential applications of spoken language systems range from simple tasks, such as retrieving information from an existing database (traffic reports, airline schedules), to interactive problem solving tasks involving complex planning and reasoning (travel planning, traffic routing), to support for multilingual interactions. We examine eight key areas in which basic research is needed to produce spoken language systems: (1) robust speech recognition; (2) automatic training and adaptation; (3) spontaneous speech; (4) dialogue models; (5) natural language response generation; (6) speech synthesis and speech generation; (7) multilingual systems; and (8) interactive multimodal systems. In each area, we identify key research challenges, the infrastructure needed to support research, and the expected benefits. We conclude by reviewing the need for multidisciplinary research, for development of shared corpora and related resources, for computational support and far rapid communication among researchers. The successful development of this technology will increase accessibility of computers to a wide range of users, will facilitate multinational communication and trade, and will create new research specialties and jobs in this rapidly expanding area

The Challenge of Spoken Language Systems: Research Directions for the Nineties

A spoken language system combines speech recognition, natural language processing and human interface technology. It functions by recognizing the person's words, interpreting the sequence of words to obtain a meaning in terms of the application, and providing an appropriate response back to the user. Potential applications of spoken language systems range from simple tasks, such as retrieving information from an existing database (traffic reports, airline schedules), to interactive problem solving tasks involving complex planning and reasoning (travel planning, traffic routing), to support for multilingual interactions. We examine eight key areas in which basic research is needed to produce spoken language systems: (1) robust speech recognition; (2) automatic training and adaptation; (3) spontaneous speech; (4) dialogue models; (5) natural language response generation; (6) speech synthesis and speech generation; (7) multilingual systems; and (8) interactive multimodal systems. In each area, we identify key research challenges, the infrastructure needed to support research, and the expected benefits. We conclude by reviewing the need for multidisciplinary research, for development of shared corpora and related resources, for computational support and far rapid communication among researchers. The successful development of this technology will increase accessibility of computers to a wide range of users, will facilitate multinational communication and trade, and will create new research specialties and jobs in this rapidly expanding area

Corpus Phonetics for Under-Documented Languages: A Vowel Harmony Example

Corpus phonetics is enabling the comprehensive analysis of large digital speech collections. In this paper, we develop a corpus phonetics workflow that is flexible enough to be easily applied to under-documented languages. To test the capabilities of this workflow we choose a challenging vowel reduction and vowel harmony problem. In Kera (Chadic) it has been shown (Pearce, 2012), that not only is phonetic reduction linked to the phonetic duration of the vowel, but also that reduction is blocked in vowel harmony domains. We are able to replicate previously published experiments by Pearce that were originally completed using manual measurements. We expect that our corpus phonetics workflow will be of value to phonologists working on other languages

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

A Silent-Speech Interface using Electro-Optical Stomatography

Sprachtechnologie ist eine große und wachsende Industrie, die das Leben von technologieinteressierten Nutzern auf zahlreichen Wegen bereichert. Viele potenzielle Nutzer werden jedoch ausgeschlossen: Nämlich alle Sprecher, die nur schwer oder sogar gar nicht Sprache produzieren können. Silent-Speech Interfaces bieten einen Weg, mit Maschinen durch ein bequemes sprachgesteuertes Interface zu kommunizieren ohne dafür akustische Sprache zu benötigen. Sie können außerdem prinzipiell eine Ersatzstimme stellen, indem sie die intendierten Äußerungen, die der Nutzer nur still artikuliert, künstlich synthetisieren. Diese Dissertation stellt ein neues Silent-Speech Interface vor, das auf einem neu entwickelten Messsystem namens Elektro-Optischer Stomatografie und einem neuartigen parametrischen Vokaltraktmodell basiert, das die Echtzeitsynthese von Sprache basierend auf den gemessenen Daten ermöglicht. Mit der Hardware wurden Studien zur Einzelworterkennung durchgeführt, die den Stand der Technik in der intra- und inter-individuellen Genauigkeit erreichten und übertrafen. Darüber hinaus wurde eine Studie abgeschlossen, in der die Hardware zur Steuerung des Vokaltraktmodells in einer direkten Artikulation-zu-Sprache-Synthese verwendet wurde. Während die Verständlichkeit der Synthese von Vokalen sehr hoch eingeschätzt wurde, ist die Verständlichkeit von Konsonanten und kontinuierlicher Sprache sehr schlecht. Vielversprechende Möglichkeiten zur Verbesserung des Systems werden im Ausblick diskutiert.:Statement of authorship iii Abstract v List of Figures vii List of Tables xi Acronyms xiii 1. Introduction 1 1.1. The concept of a Silent-Speech Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.2. Structure of this work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2. Fundamentals of phonetics 7 2.1. Components of the human speech production system . . . . . . . . . . . . . . . . . . . 7 2.2. Vowel sounds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.3. Consonantal sounds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.4. Acoustic properties of speech sounds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.5. Coarticulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.6. Phonotactics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.7. Summary and implications for the design of a Silent-Speech Interface (SSI) . . . . . . . 21 3. Articulatory data acquisition techniques in Silent-Speech Interfaces 25 3.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.2. Scope of the literature review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.3. Video Recordings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.4. Ultrasonography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.5. Electromyography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 3.6. Permanent-Magnetic Articulography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 3.7. Electromagnetic Articulography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 3.8. Radio waves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 3.9. Palatography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 3.10.Conclusion and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 4. Electro-Optical Stomatography 55 4.1. Contact sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 4.2. Optical distance sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 4.3. Lip sensor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 4.4. Sensor Unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 4.5. Control Unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 4.6. Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 5. Articulation-to-Text 99 5.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 5.2. Command word recognition pilot study . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 5.3. Command word recognition small-scale study . . . . . . . . . . . . . . . . . . . . . . . . 102 6. Articulation-to-Speech 109 6.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 6.2. Articulatory synthesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 6.3. The six point vocal tract model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 6.4. Objective evaluation of the vocal tract model . . . . . . . . . . . . . . . . . . . . . . . . 116 6.5. Perceptual evaluation of the vocal tract model . . . . . . . . . . . . . . . . . . . . . . . . 120 6.6. Direct synthesis using EOS to control the vocal tract model . . . . . . . . . . . . . . . . 125 6.7. Pitch and voicing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 7. Summary and outlook 145 7.1. Summary of the contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 7.2. Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146 A. Overview of the International Phonetic Alphabet 151 B. Mathematical proofs and derivations 153 B.1. Combinatoric calculations illustrating the reduction of possible syllables using phonotactics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 B.2. Signal Averaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 B.3. Effect of the contact sensor area on the conductance . . . . . . . . . . . . . . . . . . . . 155 B.4. Calculation of the forward current for the OP280V diode . . . . . . . . . . . . . . . . . . 155 C. Schematics and layouts 157 C.1. Schematics of the control unit. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158 C.2. Layout of the control unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 C.3. Bill of materials of the control unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164 C.4. Schematics of the sensor unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 C.5. Layout of the sensor unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166 C.6. Bill of materials of the sensor unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167 D. Sensor unit assembly 169 E. Firmware flow and data protocol 177 F. Palate file format 181 G. Supplemental material regarding the vocal tract model 183 H. Articulation-to-Speech: Optimal hyperparameters 189 Bibliography 191Speech technology is a major and growing industry that enriches the lives of technologically-minded people in a number of ways. Many potential users are, however, excluded: Namely, all speakers who cannot easily or even at all produce speech. Silent-Speech Interfaces offer a way to communicate with a machine by a convenient speech recognition interface without the need for acoustic speech. They also can potentially provide a full replacement voice by synthesizing the intended utterances that are only silently articulated by the user. To that end, the speech movements need to be captured and mapped to either text or acoustic speech. This dissertation proposes a new Silent-Speech Interface based on a newly developed measurement technology called Electro-Optical Stomatography and a novel parametric vocal tract model to facilitate real-time speech synthesis based on the measured data. The hardware was used to conduct command word recognition studies reaching state-of-the-art intra- and inter-individual performance. Furthermore, a study on using the hardware to control the vocal tract model in a direct articulation-to-speech synthesis loop was also completed. While the intelligibility of synthesized vowels was high, the intelligibility of consonants and connected speech was quite poor. Promising ways to improve the system are discussed in the outlook.:Statement of authorship iii Abstract v List of Figures vii List of Tables xi Acronyms xiii 1. Introduction 1 1.1. The concept of a Silent-Speech Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.2. Structure of this work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2. Fundamentals of phonetics 7 2.1. Components of the human speech production system . . . . . . . . . . . . . . . . . . . 7 2.2. Vowel sounds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.3. Consonantal sounds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.4. Acoustic properties of speech sounds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.5. Coarticulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.6. Phonotactics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.7. Summary and implications for the design of a Silent-Speech Interface (SSI) . . . . . . . 21 3. Articulatory data acquisition techniques in Silent-Speech Interfaces 25 3.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.2. Scope of the literature review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.3. Video Recordings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.4. Ultrasonography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.5. Electromyography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 3.6. Permanent-Magnetic Articulography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 3.7. Electromagnetic Articulography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 3.8. Radio waves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 3.9. Palatography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 3.10.Conclusion and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 4. Electro-Optical Stomatography 55 4.1. Contact sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 4.2. Optical distance sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 4.3. Lip sensor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 4.4. Sensor Unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 4.5. Control Unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 4.6. Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 5. Articulation-to-Text 99 5.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 5.2. Command word recognition pilot study . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 5.3. Command word recognition small-scale study . . . . . . . . . . . . . . . . . . . . . . . . 102 6. Articulation-to-Speech 109 6.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 6.2. Articulatory synthesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 6.3. The six point vocal tract model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 6.4. Objective evaluation of the vocal tract model . . . . . . . . . . . . . . . . . . . . . . . . 116 6.5. Perceptual evaluation of the vocal tract model . . . . . . . . . . . . . . . . . . . . . . . . 120 6.6. Direct synthesis using EOS to control the vocal tract model . . . . . . . . . . . . . . . . 125 6.7. Pitch and voicing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 7. Summary and outlook 145 7.1. Summary of the contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 7.2. Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146 A. Overview of the International Phonetic Alphabet 151 B. Mathematical proofs and derivations 153 B.1. Combinatoric calculations illustrating the reduction of possible syllables using phonotactics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 B.2. Signal Averaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 B.3. Effect of the contact sensor area on the conductance . . . . . . . . . . . . . . . . . . . . 155 B.4. Calculation of the forward current for the OP280V diode . . . . . . . . . . . . . . . . . . 155 C. Schematics and layouts 157 C.1. Schematics of the control unit. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158 C.2. Layout of the control unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 C.3. Bill of materials of the control unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164 C.4. Schematics of the sensor unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 C.5. Layout of the sensor unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166 C.6. Bill of materials of the sensor unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167 D. Sensor unit assembly 169 E. Firmware flow and data protocol 177 F. Palate file format 181 G. Supplemental material regarding the vocal tract model 183 H. Articulation-to-Speech: Optimal hyperparameters 189 Bibliography 19