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

Phonologically-Informed Speech Coding for Automatic Speech Recognition-based Foreign Language Pronunciation Training

Automatic speech recognition (ASR) and computer-assisted pronunciation training (CAPT) systems used in foreign-language educational contexts are often not developed with the specific task of second-language acquisition in mind. Systems that are built for this task are often excessively targeted to one native language (L1) or a single phonemic contrast and are therefore burdensome to train. Current algorithms have been shown to provide erroneous feedback to learners and show inconsistencies between human and computer perception. These discrepancies have thus far hindered more extensive application of ASR in educational systems. This thesis reviews the computational models of the human perception of American English vowels for use in an educational context; exploring and comparing two types of acoustic representation: a low-dimensionality linguistically-informed formant representation and more traditional Mel frequency cepstral coefficients (MFCCs). We first compare two algorithms for phoneme classification (support vector machines and long short-term memory recurrent neural networks) trained on American English vowel productions from the TIMIT corpus. We then conduct a perceptual study of non-native English vowel productions perceived by native American English speakers. We compare the results of the computational experiment and the human perception experiment to assess human/model agreement. Dissimilarities between human and model classification are explored. More phonologically-informed audio signal representations should create a more human-aligned, less L1-dependent vowel classification system with higher interpretability that can be further refined with more phonetic- and/or phonological-based research. Results show that linguistically-informed speech coding produces results that better align with human classification, supporting use of the proposed coding for ASR-based CAPT

The phonetics of second language learning and bilingualism

This chapter provides an overview of major theories and findings in the field of second language (L2) phonetics and phonology. Four main conceptual frameworks are discussed and compared: the Perceptual Assimilation Model-L2, the Native Language Magnet Theory, the Automatic Selection Perception Model, and the Speech Learning Model. These frameworks differ in terms of their empirical focus, including the type of learner (e.g., beginner vs. advanced) and target modality (e.g., perception vs. production), and in terms of their theoretical assumptions, such as the basic unit or window of analysis that is relevant (e.g., articulatory gestures, position-specific allophones). Despite the divergences among these theories, three recurring themes emerge from the literature reviewed. First, the learning of a target L2 structure (segment, prosodic pattern, etc.) is influenced by phonetic and/or phonological similarity to structures in the native language (L1). In particular, L1-L2 similarity exists at multiple levels and does not necessarily benefit L2 outcomes. Second, the role played by certain factors, such as acoustic phonetic similarity between close L1 and L2 sounds, changes over the course of learning, such that advanced learners may differ from novice learners with respect to the effect of a specific variable on observed L2 behavior. Third, the connection between L2 perception and production (insofar as the two are hypothesized to be linked) differs significantly from the perception-production links observed in L1 acquisition. In service of elucidating the predictive differences among these theories, this contribution discusses studies that have investigated L2 perception and/or production primarily at a segmental level. In addition to summarizing the areas in which there is broad consensus, the chapter points out a number of questions which remain a source of debate in the field today.https://drive.google.com/open?id=1uHX9K99Bl31vMZNRWL-YmU7O2p1tG2wHhttps://drive.google.com/open?id=1uHX9K99Bl31vMZNRWL-YmU7O2p1tG2wHhttps://drive.google.com/open?id=1uHX9K99Bl31vMZNRWL-YmU7O2p1tG2wHAccepted manuscriptAccepted manuscrip

A Deep Generative Model of Vowel Formant Typology

What makes some types of languages more probable than others? For instance, we know that almost all spoken languages contain the vowel phoneme /i/; why should that be? The field of linguistic typology seeks to answer these questions and, thereby, divine the mechanisms that underlie human language. In our work, we tackle the problem of vowel system typology, i.e., we propose a generative probability model of which vowels a language contains. In contrast to previous work, we work directly with the acoustic information -- the first two formant values -- rather than modeling discrete sets of phonemic symbols (IPA). We develop a novel generative probability model and report results based on a corpus of 233 languages.Comment: NAACL 201

A Review of Accent-Based Automatic Speech Recognition Models for E-Learning Environment

The adoption of electronics learning (e-learning) as a method of disseminating knowledge in the global educational system is growing at a rapid rate, and has created a shift in the knowledge acquisition methods from the conventional classrooms and tutors to the distributed e-learning technique that enables access to various learning resources much more conveniently and flexibly. However, notwithstanding the adaptive advantages of learner-centric contents of e-learning programmes, the distributed e-learning environment has unconsciously adopted few international languages as the languages of communication among the participants despite the various accents (mother language influence) among these participants. Adjusting to and accommodating these various accents has brought about the introduction of accents-based automatic speech recognition into the e-learning to resolve the effects of the accent differences. This paper reviews over 50 research papers to determine the development so far made in the design and implementation of accents-based automatic recognition models for the purpose of e-learning between year 2001 and 2021. The analysis of the review shows that 50% of the models reviewed adopted English language, 46.50% adopted the major Chinese and Indian languages and 3.50% adopted Swedish language as the mode of communication. It is therefore discovered that majority of the ASR models are centred on the European, American and Asian accents, while unconsciously excluding the various accents peculiarities associated with the less technologically resourced continents

vocal signal analysis in patients affected by multiple sclerosis

Abstract Multiple Sclerosis (MS) is one of the most common neurodegenerative disorder that presents specific manifestations among which the impaired speech (known also as dysarthria). The evaluation of the speech plays a crucial role in the diagnosis and follow-up since the identification of anomalous patterns in vocal signal may represent a valid support to physician in diagnosis and monitoring of these neurological diseases. In this contribution, we present a method to perform voice analysis of neurologically impaired patients affected by MS aiming to early detection, differential diagnosis, and monitoring of disease progression. This method integrates two well-known methodologies to support the health structure in MS diagnosis in clinical practice. Acoustic analysis and vowel metric methodologies have been considered to implement this procedure to better define the pathological voices compared to healthy voices. Specifically, the method acquires and analyzes vocal signals performing features extraction and identifying possible important patterns useful to associate impaired speech with this neurological disease. The contribution consists in furnishing to physician a guide method to support MS trend. As result, this method furnishes patterns that could be valid indicators for physician in monitoring of patients affected by MS. Moreover, the procedure is appropriate to be used in early diagnosis that is critical in order to improve the patient's quality of life

Dealing with linguistic mismatches for automatic speech recognition

Recent breakthroughs in automatic speech recognition (ASR) have resulted in a word error rate (WER) on par with human transcribers on the English Switchboard benchmark. However, dealing with linguistic mismatches between the training and testing data is still a significant challenge that remains unsolved. Under the monolingual environment, it is well-known that the performance of ASR systems degrades significantly when presented with the speech from speakers with different accents, dialects, and speaking styles than those encountered during system training. Under the multi-lingual environment, ASR systems trained on a source language achieve even worse performance when tested on another target language because of mismatches in terms of the number of phonemes, lexical ambiguity, and power of phonotactic constraints provided by phone-level n-grams. In order to address the issues of linguistic mismatches for current ASR systems, my dissertation investigates both knowledge-gnostic and knowledge-agnostic solutions. In the first part, classic theories relevant to acoustics and articulatory phonetics that present capability of being transferred across a dialect continuum from local dialects to another standardized language are re-visited. Experiments demonstrate the potentials that acoustic correlates in the vicinity of landmarks could help to build a bridge for dealing with mismatches across difference local or global varieties in a dialect continuum. In the second part, we design an end-to-end acoustic modeling approach based on connectionist temporal classification loss and propose to link the training of acoustics and accent altogether in a manner similar to the learning process in human speech perception. This joint model not only performed well on ASR with multiple accents but also boosted accuracies of accent identification task in comparison to separately-trained models

Short-term accommodation of Hong Kong English speakers towards native English accents and the effect of language attitudes

Accommodation, also known as convergence, refers to a process whereby a speaker changes the way he or she speaks to be more similar to another speaker. This dissertation focuses on two themes: language attitudes and short-term accommodation. A study using the matched-guise method is conducted to examine Hong Kong people’s attitudes towards British English, American English and Hong Kong English (henceforth HKE). Results suggest that after the handover British English is still rated as the most prestigious English variety in Hong Kong. HKE is also found to have a high level of acceptance in terms of social attractiveness. For short-term accommodation, two studies are conducted to investigate the phonetic convergence of HKE speakers towards native English accents, and the effect of language attitudes on convergence. Study 2 consists of a group of HKE speakers completing separate map tasks with a Received Pronunciation speaker and a General American English speaker. Their pronunciations of the THOUGHT vowel, the PATH vowel, rhoticity, fricative /z/ and fricative /θ/ are examined before, during and after the map tasks. The results suggest that the HKE speakers produce more fricative [z] and converge on rhoticity after exposure to the native accents. However, divergence is found on the PATH vowel and fricative /θ/, and maintenance is found on the THOUGHT vowel. These findings suggest that the HKE speakers tend to converge on the linguistic features which are more salient to them. Study 3 examines the effect of language attitudes on speech convergence, and no correlation is found between language attitudes and the HKE speakers’ convergence on rhoticity. Finally, the hybrid exemplar-based model is proposed to explain the complex results of the three studies. It provides a framework for speech accommodation which covers speech perception and production, and includes social factors as important elements in the model