A Comprehensive Survey of Automatic Dysarthric Speech Recognition

Automatic dysarthric speech recognition (DSR) is very crucial for many human computer interaction systems that enables the human to interact with machine in natural way. The objective of this paper is to analyze the literature survey of various Machine learning (ML) and deep learning (DL) based dysarthric speech recognition systems (DSR). This article presents a comprehensive survey of the recent advances in the automatic Dysarthric Speech Recognition (DSR) using machine learning and deep learning paradigms. It focuses on the methodology, database, evaluation metrics and major findings from the study of previous approaches.The proposed survey presents the various challenges related with DSR such as individual variability, limited training data, contextual understanding, articulation variability, vocal quality changes, and speaking rate variations.From the literature survey it provides the gaps between exiting work and previous work on DSR and provides the future direction for improvement of DSR.&nbsp

Development of Speech Command Control Based TinyML System for Post-Stroke Dysarthria Therapy Device

Post-stroke dysarthria (PSD) is a widespread outcome of a stroke. To help in the objective evaluation of dysarthria, the development of pathological voice recognition and technology has a lot of attention. Soft robotics therapy devices have been received as an alternative rehabilitation and hand grasp assistance for improving activity daily living (ADL). Despite the significant progress in this field, most soft robotic therapy devices use a complex, bulky, lack of pathological voice recognition model, large computational power, and stationary controller. This study aims to develop a portable wirelessly multi-controller with a simulated dysarthric vowel speech in Bahasa Indonesia and non-dysarthric micro speech recognition, using tiny machine learning (TinyMl) system for hardware efficiency. The speech interface using INMP441, compute with a lightweight Deep Convolutional Neural network (DCNN) design and embedded into ESP-32. Feature model using Short Time Fourier Transform (STFT) and fed into CNN. This method has proven useful in micro-speech recognition with low computational power in both speech scenarios with a level of accuracy above 90%. Realtime inference performance on ESP-32 using hand prosthetics, with 3-level household noise intensity respectively 24db,42db, and 62db, and has respectively resulted from 95%, 85%, and 50% Accuracy. Wireless connectivity success rate with both controllers is around 0.2 - 0.5 ms

The effect of multitalker background noise on speech intelligibility in Parkinson\u27s disease and controls

This study investigated the effect of multi-talker background noise on speech intelligibility in participants with hypophonia due to Parkinson’s disease (PD). Ten individuals with PD and 10 geriatric controls were tested on four speech intelligibility tasks at the single word, sentence, and conversation level in various conditions of background noise. Listeners assessed speech intelligibility using word identification or orthographic transcription procedures. Results revealed non-significant differences between groups when intelligibility was assessed in no background noise. PD speech intelligibility decreased significantly relative to controls in the presence of background noise. A phonetic error analysis revealed a distinct error profile for PD speech in background noise. The four most frequent phonetic errors were glottal-null, consonant-null in final position, stop place of articulation, and initial position cluster-singleton. The results demonstrate that individuals with PD have significant and distinctive deficits in speech intelligibility and phonetic errors in the presence of background noise

On combining acoustic and modulation spectrograms in an attention LSTM-based system for speech intelligibility level classification

Speech intelligibility can be affected by multiple factors, such as noisy environments, channel distortions or physiological issues. In this work, we deal with the problem of automatic prediction of the speech intelligibility level in this latter case. Starting from our previous work, a non-intrusive system based on LSTM networks with attention mechanism designed for this task, we present two main contributions. In the first one, it is proposed the use of per-frame modulation spectrograms as input features, instead of compact representations derived from them that discard important temporal information. In the second one, two different strategies for the combination of per-frame acoustic log-mel and modulation spectrograms into the LSTM framework are explored: at decision level or late fusion and at utterance level or Weighted-Pooling (WP) fusion. The proposed models are evaluated with the UA-Speech database that contains dysarthric speech with different degrees of severity. On the one hand, results show that attentional LSTM networks are able to adequately modeling the modulation spectrograms sequences producing similar classification rates as in the case of log-mel spectrograms. On the other hand, both combination strategies, late and WP fusion, outperform the single-feature systems, suggesting that per-frame log-mel and modulation spectrograms carry complementary information for the task of speech intelligibility prediction, than can be effectively exploited by the LSTM-based architectures, being the system with the WP fusion strategy and Attention-Pooling the one that achieves best results.The work leading to these results has been partly supported by the Spanish Government-MinECo under Projects TEC2017-84395-P and TEC2017-84593-C2-1-R.Publicad

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

Improving Dysarthric Speech Recognition by Enriching Training Datasets

Dysarthria is a motor speech disorder that results from disruptions in the neuro-motor interface and is characterised by poor articulation of phonemes and hyper-nasality and is characteristically different from normal speech. Many modern automatic speech recognition systems focus on a narrow range of speech diversity therefore as a consequence of this they exclude a groups of speakers who deviate in aspects of gender, race, age and speech impairment when building training datasets. This study attempts to develop an automatic speech recognition system that deals with dysarthric speech with limited dysarthric speech data. Speech utterances collected from the TORGO database are used to conduct experiments on a wav2vec2.0 model only trained on the Librispeech 960h dataset to obtain a baseline performance of the word error rate (WER) when recognising dysarthric speech. A version of the Librispeech model fine-tuned on multi-language datasets was tested to see if it would improve accuracy and achieved a top reduction of 24.15% in the WER for one of the male dysarthric speakers in the dataset. Transfer learning with speech recognition models and preprocessing dysarthric speech to improve its intelligibility by using general adversarial networks were limited in their potential due to a lack of dysarthric speech dataset of adequate size to use these technologies. The main conclusion drawn from this study is that a large diverse dysarthric speech dataset comparable to the size of datasets used to train machine learning ASR systems like Librispeech,with different types of speech, scripted and unscripted, is required to improve performance.

CDSD: Chinese Dysarthria Speech Database

We present the Chinese Dysarthria Speech Database (CDSD) as a valuable resource for dysarthria research. This database comprises speech data from 24 participants with dysarthria. Among these participants, one recorded an additional 10 hours of speech data, while each recorded one hour, resulting in 34 hours of speech material. To accommodate participants with varying cognitive levels, our text pool primarily consists of content from the AISHELL-1 dataset and speeches by primary and secondary school students. When participants read these texts, they must use a mobile device or the ZOOM F8n multi-track field recorder to record their speeches. In this paper, we elucidate the data collection and annotation processes and present an approach for establishing a baseline for dysarthric speech recognition. Furthermore, we conducted a speaker-dependent dysarthric speech recognition experiment using an additional 10 hours of speech data from one of our participants. Our research findings indicate that, through extensive data-driven model training, fine-tuning limited quantities of specific individual data yields commendable results in speaker-dependent dysarthric speech recognition. However, we observe significant variations in recognition results among different dysarthric speakers. These insights provide valuable reference points for speaker-dependent dysarthric speech recognition.Comment: 9 pages, 3 figure

Confusion modelling for lip-reading

Lip-reading is mostly used as a means of communication by people with hearing di�fficulties. Recent work has explored the automation of this process, with the aim of building a speech recognition system entirely driven by lip movements. However, this work has so far produced poor results because of factors such as high variability of speaker features, diffi�culties in mapping from visual features to speech sounds, and high co-articulation of visual features. The motivation for the work in this thesis is inspired by previous work in dysarthric speech recognition [Morales, 2009]. Dysathric speakers have poor control over their articulators, often leading to a reduced phonemic repertoire. The premise of this thesis is that recognition of the visual speech signal is a similar problem to recog- nition of dysarthric speech, in that some information about the speech signal has been lost in both cases, and this brings about a systematic pattern of errors in the decoded output. This work attempts to exploit the systematic nature of these errors by modelling them in the framework of a weighted finite-state transducer cascade. Results indicate that the technique can achieve slightly lower error rates than the conventional approach. In addition, it explores some interesting more general questions for automated lip-reading

A computational model of the relationship between speech intelligibility and speech acoustics

abstract: Speech intelligibility measures how much a speaker can be understood by a listener. Traditional measures of intelligibility, such as word accuracy, are not sufficient to reveal the reasons of intelligibility degradation. This dissertation investigates the underlying sources of intelligibility degradations from both perspectives of the speaker and the listener. Segmental phoneme errors and suprasegmental lexical boundary errors are developed to reveal the perceptual strategies of the listener. A comprehensive set of automated acoustic measures are developed to quantify variations in the acoustic signal from three perceptual aspects, including articulation, prosody, and vocal quality. The developed measures have been validated on a dysarthric speech dataset with various severity degrees. Multiple regression analysis is employed to show the developed measures could predict perceptual ratings reliably. The relationship between the acoustic measures and the listening errors is investigated to show the interaction between speech production and perception. The hypothesize is that the segmental phoneme errors are mainly caused by the imprecise articulation, while the sprasegmental lexical boundary errors are due to the unreliable phonemic information as well as the abnormal rhythm and prosody patterns. To test the hypothesis, within-speaker variations are simulated in different speaking modes. Significant changes have been detected in both the acoustic signals and the listening errors. Results of the regression analysis support the hypothesis by showing that changes in the articulation-related acoustic features are important in predicting changes in listening phoneme errors, while changes in both of the articulation- and prosody-related features are important in predicting changes in lexical boundary errors. Moreover, significant correlation has been achieved in the cross-validation experiment, which indicates that it is possible to predict intelligibility variations from acoustic signal.Dissertation/ThesisDoctoral Dissertation Speech and Hearing Science 201

Speech Communication

Contains table of contents for Part V, table of contents for Section 1, reports on six research projects and a list of publications.C.J. Lebel FellowshipDennis Klatt Memorial FundNational Institutes of Health Grant R01-DC00075National Institutes of Health Grant R01-DC01291National Institutes of Health Grant R01-DC01925National Institutes of Health Grant R01-DC02125National Institutes of Health Grant R01-DC02978National Institutes of Health Grant R01-DC03007National Institutes of Health Grant R29-DC02525National Institutes of Health Grant F32-DC00194National Institutes of Health Grant F32-DC00205National Institutes of Health Grant T32-DC00038National Science Foundation Grant IRI 89-05249National Science Foundation Grant IRI 93-14967National Science Foundation Grant INT 94-2114