Automatic assessment of intelligibility in speakers with dysarthria from coded telephone speech using glottal features

Narendra, N. P.; Alku, Paavo

doi:10.1016/j.csl.2020.101117

Cited by 28 publications

(9 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this scenario, we observed that the HMLM overcomes these aspects performing much better than the widely adopted flat multi-class approach. Despite these limitations, it's also important to say that the collected structured dataset is the first and the biggest released till date, and there are a few works in this field [33]. As evidenced from results of cross validation matrix (Fig.…”

Section: Discussionmentioning

confidence: 99%

“…As already suggested in [35], to assess dysarthria in children with ataxia, artificial intelligence has shown very promising results. A fundamental step is the extraction of all features that can be used as input parameters in disorder characterization systems [33], [34]. The aim is to identify the relevant information contained in the speech signal.…”

Section: Introductionmentioning

confidence: 99%

“…An automatic intelligibility assessment system that performs a binary classification by capturing atypical variation in dysarthric speech by using linear discriminant analysis (LDA), k-nearest neighbor (KNN) and SVM classifiers was proposed [36] with an accuracy of 68%, 66% and 70% respectively. While in [33] four levels of intelligibility were recognized with an accuracy between 40-50%, using SVMs and testing different feature sets. Moreover, the combination of the statistical GMM and ANNs was used in [38], achieving accuracy of 86% over three degrees of severity levels.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Artificial Intelligence for Dysarthria Assessment in Children With Ataxia: A Hierarchical Approach

et al. 2021

View full text Add to dashboard Cite

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Artificial Intelligence for Dysarthria Assessment in Children With Ataxia: A Hierarchical Approach

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Furthermore, MFCCs derived from the glottal source waveforms were shown to be effective for voice pathology detection. In [26], [262], and [263], it was shown that glottal source features were useful in the automatic detection of dysarthria and also in the assessment of intelligibility in speakers with dysarthria. In [263], glottal parameters computed by GIF were used to identify pathophysiological phonatory mechanisms for phonotraumatic and nonphonotraumatic vocal hyperfunction.…”

Section: Study Of Pathological Voicesmentioning

confidence: 99%

Extraction and Utilization of Excitation Information of Speech: A Review

2021

Self Cite

View full text Add to dashboard Cite

| Speech production can be regarded as a process where a time-varying vocal tract system (filter) is excited by a time-varying excitation. In addition to its linguistic message, the speech signal also carries information about, for example, the gender and age of the speaker. Moreover, the speech signal includes acoustical cues about several speaker traits, such as the emotional state and the state of health of the speaker. In order to understand the production of these acoustical cues by the human speech production mechanism and utilize this information in speech technology, it is necessary to extract features describing both the excitation and the filter of the human speech production mechanism. While the methods to estimate and parameterize the vocal tract system are well established, the excitation appears less studied. This article provides a review of signal processing approaches used for the extraction of excitation information from speech. This article highlights the importance of excitation information in the analysis and classification of phonation type and vocal emotions, in the analysis of nonverbal laughter sounds, and in studying pathological voices. Furthermore, recent developments of deep learning techniques in the context of extraction and utilization of the excitation information are discussed.

show abstract

“…Since dysarthric speech can be significantly unintelligible [3], a typical audience may face difficulties communicating This paragraph of the first footnote will contain the date on which you submitted your paper for review. It will also contain support information, including sponsor and financial support acknowledgment.…”

Section: Introductionmentioning

confidence: 99%

Speech Vision: An End-to-End Deep Learning-Based Dysarthric Automatic Speech Recognition System

Shahamiri

2021

IEEE Trans. Neural Syst. Rehabil. Eng.

View full text Add to dashboard Cite

Dysarthria is a disorder that affects an individual's speech intelligibility due to the paralysis of muscles and organs involved in the articulation process. As the condition is often associated with physically debilitating disabilities, not only do such individuals face communication problems, but also interactions with digital devices can become a burden. For these individuals, automatic speech recognition (ASR) technologies can make a significant difference in their lives as computing and portable digital devices can become an interaction medium, enabling them to communicate with others and computers. However, ASR technologies have performed poorly in recognizing dysarthric speech, especially for severe dysarthria, due to multiple challenges facing dysarthric ASR systems. We identified these challenges are due to the alternation and inaccuracy of dysarthric phonemes, the scarcity of dysarthric speech data, and the phoneme labeling imprecision. This paper reports on our second dysarthric-specific ASR system, called Speech Vision (SV) that tackles these challenges by adopting a novel approach towards dysarthric ASR in which speech features are extracted visually, then SV learns to see the shape of the words pronounced by dysarthric individuals. This visual acoustic modeling feature of SV eliminates phonemerelated challenges. To address the data scarcity problem, SV adopts visual data augmentation techniques, generates synthetic dysarthric acoustic visuals, and leverages transfer learning. Benchmarking with other state-of-the-art dysarthric ASR considered in this study, SV outperformed them by improving recognition accuracies for 67% of UA-Speech speakers, where the biggest improvements were achieved for severe dysarthria.

show abstract

Automatic assessment of intelligibility in speakers with dysarthria from coded telephone speech using glottal features

Cited by 28 publications

References 48 publications

Artificial Intelligence for Dysarthria Assessment in Children With Ataxia: A Hierarchical Approach

Artificial Intelligence for Dysarthria Assessment in Children With Ataxia: A Hierarchical Approach

Extraction and Utilization of Excitation Information of Speech: A Review

Speech Vision: An End-to-End Deep Learning-Based Dysarthric Automatic Speech Recognition System

Contact Info

Product

Resources

About