Syllable Classification Using Articulatory-Acoustic Features

This paper investigates the use of articulatory-acoustic features for the classification of syllables in TIMIT. The main motivation for this study is to circumvent the "beads-on-a-string" problem, i.e. the assumption that words can be described as a simple concatenation of phones. Posterior probabilities for articulatory-acoustic features are obtained from artificial neural nets and are used to classify speech within the scope of syllables instead of phones. This gives the opportunity to account for asynchronous feature changes, exploiting the strengths of the articulatory-acoustic features, instead of losing the potential by reverting to phones

Syllable classification using static matrices and prosodic features

In this paper we explore the usefulness of prosodic features for syllable classification. In order to do this, we represent the syllable as a static analysis unit such that its acoustic-temporal dynamics could be merged into a set of features that the SVM classifier will consider as a whole. In the first part of our experiment we used MFCC as features for classification, obtaining a maximum accuracy of 86.66%. The second part of our study tests whether the prosodic information is complementary to the cepstral information for syllable classification. The results obtained show that combining the two types of information does improve the classification, but further analysis is necessary for a more successful combination of the two types of features

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

A silent speech system based on permanent magnet articulography and direct synthesis

In this paper we present a silent speech interface (SSI) system aimed at restoring speech communication for individuals who have lost their voice due to laryngectomy or diseases affecting the vocal folds. In the proposed system, articulatory data captured from the lips and tongue using permanent magnet articulography (PMA) are converted into audible speech using a speaker-dependent transformation learned from simultaneous recordings of PMA and audio signals acquired before laryngectomy. The transformation is represented using a mixture of factor analysers, which is a generative model that allows us to efficiently model non-linear behaviour and perform dimensionality reduction at the same time. The learned transformation is then deployed during normal usage of the SSI to restore the acoustic speech signal associated with the captured PMA data. The proposed system is evaluated using objective quality measures and listening tests on two databases containing PMA and audio recordings for normal speakers. Results show that it is possible to reconstruct speech from articulator movements captured by an unobtrusive technique without an intermediate recognition step. The SSI is capable of producing speech of sufficient intelligibility and naturalness that the speaker is clearly identifiable, but problems remain in scaling up the process to function consistently for phonetically rich vocabularies

Segmental alignment of English syllables with singleton and cluster onsets

Recent research has shown fresh evidence that consonant and vowel are synchronised at the syllable onset, as predicted by a number of theoretical models. The finding was made by using a minimal contrast paradigm to determine segment onset in Mandarin CV syllables, which differed from the conventional method of detecting gesture onset with a velocity threshold [1]. It has remained unclear, however, if CV co-onset also occurs between the nucleus vowel and a consonant cluster, as predicted by the articulatory syllable model [2]. This study applied the minimal contrast paradigm to British English in both CV and clusterV (CLV) syllables, and analysed the spectral patterns with signal chopping in conjunction with recurrent neural networks (RNN) with long short-term memory (LSTM) [3]. Results show that vowel onset is synchronised with the onset of the first consonant in a cluster, thus supporting the articulatory syllable model

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