Dynamic Estimation of Phoneme Confusion Patterns with a Genetic Algorithm to Improve the Performance of Metamodels for Recognition of Disordered Speech

Morales, Santiago; Trujillo-Romero, Felipe

doi:10.1007/978-3-642-37798-3_16

Cited by 2 publications

(6 citation statements)

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“…Future work is focused on extending the study of the situations observed in this work: to explore on the use of dynamic topologies, where besides changing the number of states, the transitions between them can also be changed; to improve the convergence of the micro-GA with alternative crossover and mutation operators; to test the approach on a different and larger database of dysarthric speech (e.g., the TORGO database); to incorporate the post-processing confusion-matrix approach presented in [ 18 , 26 , 38 ] for further improvement (for this, more dysarthric speech data would be required); to explore on the use of the HMM assignations for the assessment of dysarthric speech. …”

Section: Discussion and Future Workmentioning

confidence: 99%

“…to incorporate the post-processing confusion-matrix approach presented in [ 18 , 26 , 38 ] for further improvement (for this, more dysarthric speech data would be required);…”

Section: Discussion and Future Workmentioning

confidence: 99%

“…High ASR performance is obtained with a number of Gaussian mixture components within the range of eight [38] to 16 [45]. However, usually less than ten Gaussian mixture components are used [35].…”

Section: Hmm Parameters For Optimizationmentioning

confidence: 99%

“…The number of Gaussian PDFs (mixture components) affects the performance of a HMM-based ASR system [ 4 , 43 , 45 ]. High ASR performance is obtained with a number of Gaussian mixture components within the range of eight [ 38 ] to 16 [ 45 ]. However, usually less than ten Gaussian mixture components are used [ 35 ].…”

Section: Hmm Parameters For Optimizationmentioning

confidence: 99%

“…A previous approach considered the response of the ASR system to estimate the phoneme confusion patterns of the speaker's speech. Then, this information was integrated into the ASR process to correct those confusion errors (deletion, substitution, and/or insertion of phonemes) and provide a more accurate response [ 18 , 26 , 36 – 38 ]. This approach performed better than other approaches that made use of speaker adaptation techniques (as those used by commercial ASR systems) because, as commented in [ 39 ], these are insufficient to deal with the abnormalities present in dysarthric speech.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Phoneme-Specific HMM Topologies for the Automatic Recognition of Dysarthric Speech

Caballero‐Morales

2013

Computational and Mathematical Methods in Medicine

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Dysarthria is a frequently occurring motor speech disorder which can be caused by neurological trauma, cerebral palsy, or degenerative neurological diseases. Because dysarthria affects phonation, articulation, and prosody, spoken communication of dysarthric speakers gets seriously restricted, affecting their quality of life and confidence. Assistive technology has led to the development of speech applications to improve the spoken communication of dysarthric speakers. In this field, this paper presents an approach to improve the accuracy of HMM-based speech recognition systems. Because phonatory dysfunction is a main characteristic of dysarthric speech, the phonemes of a dysarthric speaker are affected at different levels. Thus, the approach consists in finding the most suitable type of HMM topology (Bakis, Ergodic) for each phoneme in the speaker's phonetic repertoire. The topology is further refined with a suitable number of states and Gaussian mixture components for acoustic modelling. This represents a difference when compared with studies where a single topology is assumed for all phonemes. Finding the suitable parameters (topology and mixtures components) is performed with a Genetic Algorithm (GA). Experiments with a well-known dysarthric speech database showed statistically significant improvements of the proposed approach when compared with the single topology approach, even for speakers with severe dysarthria.

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Section: Discussion and Future Workmentioning

confidence: 99%

“…to incorporate the post-processing confusion-matrix approach presented in [ 18 , 26 , 38 ] for further improvement (for this, more dysarthric speech data would be required);…”

Section: Discussion and Future Workmentioning

confidence: 99%

Section: Hmm Parameters For Optimizationmentioning

confidence: 99%

Section: Hmm Parameters For Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimation of Phoneme-Specific HMM Topologies for the Automatic Recognition of Dysarthric Speech

Caballero‐Morales

2013

Computational and Mathematical Methods in Medicine

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An evaluation of unsupervised acoustic model training for a dysarthric speech interface

Walter

Despotović²,

Haeb‐Umbach³

et al. 2014

Interspeech 2014

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In this paper, we investigate unsupervised acoustic model training approaches for dysarthric-speech recognition. These models are first, frame-based Gaussian posteriorgrams, obtained from Vector Quantization (VQ), second, so-called Acoustic Unit Descriptors (AUDs), which are hidden Markov models of phone-like units, that are trained in an unsupervised fashion, and, third, posteriorgrams computed on the AUDs. Experiments were carried out on a database collected from a home automation task and containing nine speakers, of which seven are considered to utter dysarthric speech. All unsupervised modeling approaches delivered significantly better recognition rates than a speaker-independent phoneme recognition baseline, showing the suitability of unsupervised acoustic model training for dysarthric speech. While the AUD models led to the most compact representation of an utterance for the subsequent semantic inference stage, posteriorgram-based representations resulted in higher recognition rates, with the Gaussian posteriorgram achieving the highest slot filling F-score of 97.02%.

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Dynamic Estimation of Phoneme Confusion Patterns with a Genetic Algorithm to Improve the Performance of Metamodels for Recognition of Disordered Speech

Cited by 2 publications

References 12 publications

Estimation of Phoneme-Specific HMM Topologies for the Automatic Recognition of Dysarthric Speech

Estimation of Phoneme-Specific HMM Topologies for the Automatic Recognition of Dysarthric Speech

An evaluation of unsupervised acoustic model training for a dysarthric speech interface

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