Consonant landmark detection for speech recognition

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 191-197).This thesis focuses on the detection of abrupt acoustic discontinuities in the speech signal, which constitute landmarks for consonant sounds. Because a large amount of phonetic information is concentrated near acoustic discontinuities, more focused speech analysis and recognition can be performed based on the landmarks. Three types of consonant landmarks are defined according to its characteristics -- glottal vibration, turbulence noise, and sonorant consonant -- so that the appropriate analysis method for each landmark point can be determined. A probabilistic knowledge-based algorithm is developed in three steps. First, landmark candidates are detected and their landmark types are classified based on changes in spectral amplitude. Next, a bigram model describing the physiologically-feasible sequences of consonant landmarks is proposed, so that the most likely landmark sequence among the candidates can be found. Finally, it has been observed that certain landmarks are ambiguous in certain sets of phonetic and prosodic contexts, while they can be reliably detected in other contexts. A method to represent the regions where the landmarks are reliably detected versus where they are ambiguous is presented. On TIMIT test set, 91% of all the consonant landmarks and 95% of obstruent landmarks are located as landmark candidates. The bigram-based process for determining the most likely landmark sequences yields 12% deletion and substitution rates and a 15% insertion rate. An alternative representation that distinguishes reliable and ambiguous regions can detect 92% of the landmarks and 40% of the landmarks are judged to be reliable. The deletion rate within reliable regions is as low as 5%.(cont.) The resulting landmark sequences form a basis for a knowledge-based speech recognition system since the landmarks imply broad phonetic classes of the speech signal and indicate the points of focus for estimating detailed phonetic information. In addition, because the reliable regions generally correspond to lexical stresses and word boundaries, it is expected that the landmarks can guide the focus of attention not only at the phoneme-level, but at the phrase-level as well.by Chiyoun Park.Ph.D

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

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

Models and Analysis of Vocal Emissions for Biomedical Applications

The International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA) came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the neonate to the adult and elderly. Over the years the initial issues have grown and spread also in other aspects of research such as occupational voice disorders, neurology, rehabilitation, image and video analysis. MAVEBA takes place every two years always in Firenze, Italy

A syllable-based investigation of coarticulation

Coarticulation has been long investigated in Speech Sciences and Linguistics (Kühnert & Nolan, 1999). This thesis explores coarticulation through a syllable based model (Y. Xu, 2020). First, it is hypothesised that consonant and vowel are synchronised at the syllable onset for the sake of reducing temporal degrees of freedom, and such synchronisation is the essence of coarticulation. Previous efforts in the examination of CV alignment mainly report onset asynchrony (Gao, 2009; Shaw & Chen, 2019). The first study of this thesis tested the synchrony hypothesis using articulatory and acoustic data in Mandarin. Departing from conventional approaches, a minimal triplet paradigm was applied, in which the CV onsets were determined through the consonant and vowel minimal pairs, respectively. Both articulatory and acoustical results showed that CV articulation started in close temporal proximity, supporting the synchrony hypothesis. The second study extended the research to English and syllables with cluster onsets. By using acoustic data in conjunction with Deep Learning, supporting evidence was found for co-onset, which is in contrast to the widely reported c-center effect (Byrd, 1995). Secondly, the thesis investigated the mechanism that can maximise synchrony – Dimension Specific Sequential Target Approximation (DSSTA), which is highly relevant to what is commonly known as coarticulation resistance (Recasens & Espinosa, 2009). Evidence from the first two studies show that, when conflicts arise due to articulation requirements between CV, the CV gestures can be fulfilled by the same articulator on separate dimensions simultaneously. Last but not least, the final study tested the hypothesis that resyllabification is the result of coarticulation asymmetry between onset and coda consonants. It was found that neural network based models could infer syllable affiliation of consonants, and those inferred resyllabified codas had similar coarticulatory structure with canonical onset consonants. In conclusion, this thesis found that many coarticulation related phenomena, including local vowel to vowel anticipatory coarticulation, coarticulation resistance, and resyllabification, stem from the articulatory mechanism of the syllable

Deep learning assessment of syllable affiliation of intervocalic consonants

In English, a sentence like “He made out our intentions.” could be misperceived as “He may doubt our intentions.” because the coda /d/ sounds like it has become the onset of the next syllable. The nature and occurrence condition of this resyllabification phenomenon are unclear, however. Previous empirical studies mainly relied on listener judgment, limited acoustic evidence, such as voice onset time, or average formant values to determine the occurrence of resyllabification. This study tested the hypothesis that resyllabification is a coarticulatory reorganisation that realigns the coda consonant with the vowel of the next syllable. Deep learning in conjunction with dynamic time warping (DTW) was used to assess syllable affiliation of intervocalic consonants. The results suggest that convolutional neural network- and recurrent neural network-based models can detect cases of resyllabification using Mel-frequency spectrograms. DTW analysis shows that neural network inferred resyllabified sequences are acoustically more similar to their onset counterparts than their canonical productions. A binary classifier further suggests that, similar to the genuine onsets, the inferred resyllabified coda consonants are coarticulated with the following vowel. These results are interpreted with an account of resyllabification as a speech-rate-dependent coarticulatory reorganisation mechanism in speech

Optimization of acoustic feature extraction from dysarthric speech

Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, February 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 171-180).Dysarthria is a motor speech disorder characterized by weak or uncoordinated movements of the speech musculature. While unfamiliar listeners struggle to understand speakers with severe dysarthria, familiar listeners are often able to comprehend with high accuracy. This observation implies that although the speech produced by an individual with dysarthria may appear distorted and unintelligible to the untrained listener, there must be a set of consistent acoustic cues that the familiar communication partner is able to interpret. While dysarthric speech has been characterized both acoustically and perceptually, most accounts tend to compare dysarthric productions to those of healthy controls rather than identify the set of reliable and consistently controlled segmental cues. This work aimed to elucidate possible recognition strategies used by familiar listeners by optimizing a model of human speech recognition, Stevens' Lexical Access from Features (LAFF) framework, for ten individual speakers with dysarthria (SWDs). The LAFF model is rooted in distinctive feature theory, with acoustic landmarks indicating changes in the manner of articulation. The acoustic correlates manifested around landmarks provide the identity to articulator-free (manner) and articulator-bound (place) features.(cont.) SWDs created weaker consonantal landmarks, likely due to an inability to form complete closures in the vocal tract and to fully release consonantal constrictions. Identification of speaker-optimized acoustic correlate sets improved discrimination of each speaker's productions, evidenced by increased sensitivity and specificity. While there was overlap between the types of correlates identified for healthy and dysarthric speakers, using the optimal sets of correlates identified for SWDs adversely impaired discrimination of healthy speech. These results suggest that the combinations of correlates suggested for SWDs were specific to the individual and different from the segmental cues used by healthy individuals. Application of the LAFF model to dysarthric speech has potential clinical utility as a diagnostic tool, highlighting the fine-grain components of speech production that require intervention and quantifying the degree of impairment.by Thomas M. DiCicco, Jr.Ph.D

Models and Analysis of Vocal Emissions for Biomedical Applications

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies

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

An exploration of the rhythm of Malay

In recent years there has been a surge of interest in speech rhythm. However we still lack a clear understanding of the nature of rhythm and rhythmic differences across languages. Various metrics have been proposed as means for measuring rhythm on the phonetic level and making typological comparisons between languages (Ramus et al, 1999; Grabe & Low, 2002; Dellwo, 2006) but the debate is ongoing on the extent to which these metrics capture the rhythmic basis of speech (Arvaniti, 2009; Fletcher, in press). Furthermore, cross linguistic studies of rhythm have covered a relatively small number of languages and research on previously unclassified languages is necessary to fully develop the typology of rhythm. This study examines the rhythmic features of Malay, for which, to date, relatively little work has been carried out on aspects rhythm and timing. The material for the analysis comprised 10 sentences produced by 20 speakers of standard Malay (10 males and 10 females). The recordings were first analysed using rhythm metrics proposed by Ramus et. al (1999) and Grabe & Low (2002). These metrics (∆C, %V, rPVI, nPVI) are based on durational measurements of vocalic and consonantal intervals. The results indicated that Malay clustered with other so-called syllable-timed languages like French and Spanish on the basis of all metrics. However, underlying the overall findings for these metrics there was a large degree of variability in values across speakers and sentences, with some speakers having values in the range typical of stressed-timed languages like English. Further analysis has been carried out in light of Fletcher’s (in press) argument that measurements based on duration do not wholly reflect speech rhythm as there are many other factors that can influence values of consonantal and vocalic intervals, and Arvaniti’s (2009) suggestion that other features of speech should also be considered in description of rhythm to discover what contributes to listeners’ perception of regularity. Spectrographic analysis of the Malay recordings brought to light two parameters that displayed consistency and regularity for all speakers and sentences: the duration of individual vowels and the duration of intervals between intensity minima. This poster presents the results of these investigations and points to connections between the features which seem to be consistently regulated in the timing of Malay connected speech and aspects of Malay phonology. The results are discussed in light of current debate on the descriptions of rhythm