Abstract
Dysarthria is a motor speech disorder characterized by weakness, paralysis, or poor coordination of the muscles responsible for speech. Although automatic speech recognition (ASR) systems have been developed for disordered speech, factors such as low intelligibility and limited phonemic repertoire decrease speech recognition accuracy, making conventional speaker adaptation algorithms perform poorly on dysarthric speakers. In this work, rather than adapting the acoustic models, we model the errors made by the speaker and attempt to correct them. For this task, two techniques have been developed: (1) a set of "metamodels" that incorporate a model of the speaker's phonetic confusion matrix into the ASR process; (2) a cascade of weighted finite-state transducers at the confusion matrix, word, and language levels. Both techniques attempt to correct the errors made at the phonetic level and make use of a language model to find the best estimate of the correct word sequence. Our experiments show that both techniques outperform standard adaptation techniques.
Highlights
“Dysarthria is a motor speech disorder that is often associated with irregular phonation and amplitude, incoordination of articulators, and restricted movement of articulators” [1]
The matched pairs test described in [41] was used to test for significant differences between the recognition accuracy using metamodels and the accuracy obtained with MLLR adaptation when a certain number of sentences were available for metamodel training
We have argued that in the case of dysarthric speakers, who have a limited phonemic repertoire and consistently substitute certain phonemes for others, modelling and correcting the errors made by the speaker under the guidance of a language model is a more effective approach than adapting acoustic models in the way that is effective for normal speakers
Summary
“Dysarthria is a motor speech disorder that is often associated with irregular phonation and amplitude, incoordination of articulators, and restricted movement of articulators” [1]. Six impairment features are related to phonatory dysfunction, reducing the speaker’s intelligibility and altering naturalness of his/her speech [4, 7, 8] These features make the task of developing assistive Automatic Speech Recognition (ASR) systems for people with dysarthria very challenging. It is important to develop ASR systems for dysarthric speakers because of the advantages they offer when compared with interfaces such as switches or keyboards These may be more physically demanding and tiring [14,15,16,17] and as dysarthria is usually accompanied by other physical handicaps, impossible for them to use. Even with the speech production difficulties exhibited by many of these speakers, speech communication requires less effort and is faster than conventional typing methods [18], despite the difficulty of achieving robust recognition performance
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