Dataset Information

Assessment of Dysarthria Using One-Word Speech Recognition with Hidden Markov Models.

ABSTRACT:

Background

The gold standard in dysarthria assessment involves subjective analysis by a speech-language pathologist (SLP). We aimed to investigate the feasibility of dysarthria assessment using automatic speech recognition.

Methods

We developed an automatic speech recognition based software to assess dysarthria severity using hidden Markov models (HMMs). Word-specific HMMs were trained using the utterances from one hundred healthy individuals. Twenty-eight patients with dysarthria caused by neurological disorders, including stroke, traumatic brain injury, and Parkinson's disease were participated and their utterances were recorded. The utterances of 37 words from the Assessment of Phonology and Articulation for Children test were recorded in a quiet control booth in both groups. Patients were asked to repeat the recordings for evaluating the test-retest reliability. Patients' utterances were evaluated by two experienced SLPs, and the consonant production accuracy was calculated as a measure of dysarthria severity. The trained HMMs were also employed to evaluate the patients' utterances by calculating the averaged log likelihood (aLL) as the fitness of the spoken word to the word-specific HMM.

Results

The consonant production accuracy reported by the SLPs strongly correlated (r = 0.808) with the aLL, and the aLL showed excellent test-retest reliability (intraclass correlation coefficient, 0.964).

Conclusion

This leads to the conclusion that dysarthria assessment using a one-word speech recognition system based on word-specific HMMs is feasible in neurological disorders.

SUBMITTER: Lee SH

PROVIDER: S-EPMC6449601 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Publications

Assessment of Dysarthria Using One-Word Speech Recognition with Hidden Markov Models.

Lee Seung Hak SH Kim Minje M Seo Han Gil HG Oh Byung Mo BM Lee Gangpyo G Leigh Ja Ho JH

Journal of Korean medical science 20190408 13

<h4>Background</h4>The gold standard in dysarthria assessment involves subjective analysis by a speech-language pathologist (SLP). We aimed to investigate the feasibility of dysarthria assessment using automatic speech recognition.<h4>Methods</h4>We developed an automatic speech recognition based software to assess dysarthria severity using hidden Markov models (HMMs). Word-specific HMMs were trained using the utterances from one hundred healthy individuals. Twenty-eight patients with dysarthria ...[more]

PMID: 30950253

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Project description:BackgroundEukaryotic gene regulation is a complex process comprising the dynamic interaction of enhancers and promoters in order to activate gene expression. In recent years, research in regulatory genomics has contributed to a better understanding of the characteristics of promoter elements and for most sequenced model organism genomes there exist comprehensive and reliable promoter annotations. For enhancers, however, a reliable description of their characteristics and location has so far proven to be elusive. With the development of high-throughput methods such as ChIP-seq, large amounts of data about epigenetic conditions have become available, and many existing methods use the information on chromatin accessibility or histone modifications to train classifiers in order to segment the genome into functional groups such as enhancers and promoters. However, these methods often do not consider prior biological knowledge about enhancers such as their diverse lengths or molecular structure.ResultsWe developed enhancer HMM (eHMM), a supervised hidden Markov model designed to learn the molecular structure of promoters and enhancers. Both consist of a central stretch of accessible DNA flanked by nucleosomes with distinct histone modification patterns. We evaluated the performance of eHMM within and across cell types and developmental stages and found that eHMM successfully predicts enhancers with high precision and recall comparable to state-of-the-art methods, and consistently outperforms those in terms of accuracy and resolution.ConclusionseHMM predicts active enhancers based on data from chromatin accessibility assays and a minimal set of histone modification ChIP-seq experiments. In comparison to other 'black box' methods its parameters are easy to interpret. eHMM can be used as a stand-alone tool for enhancer prediction without the need for additional training or a tuning of parameters. The high spatial precision of enhancer predictions gives valuable targets for potential knockout experiments or downstream analyses such as motif search.

Dataset Information

Assessment of Dysarthria Using One-Word Speech Recognition with Hidden Markov Models.

Background

Methods

Results

Conclusion

Publications

Assessment of Dysarthria Using One-Word Speech Recognition with Hidden Markov Models.

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