Severity-based adaptation with limited data for ASR to aid dysarthric speakers.
Automatic speech recognition (ASR) is currently used in many assistive technologies, such as helping individuals with speech impairment in their communication ability. One challenge in ASR for speech-impaired individuals is the difficulty in obtaining a good speech database of impaired speakers for building an effective speech acoustic model. Because there are very few existing databases of impaired speech, which are also limited in size, the obvious solution to build a speech acoustic model of impaired speech is by employing adaptation techniques. However, issues that have not been addressed in existing studies in the area of adaptation for speech impairment are as follows: (1) identifying the most effective adaptation technique for impaired speech; and (2) the use of suitable source models to build an effective impaired-speech acoustic model. This research investigates the above-mentioned two issues on dysarthria, a type of speech impairment affecting millions of people. We applied both unimpaired and impaired speech as the source model with well-known adaptation techniques like the maximum likelihood linear regression (MLLR) and the constrained-MLLR(C-MLLR). The recognition accuracy of each impaired speech acoustic model is measured in terms of word error rate (WER), with further assessments, including phoneme insertion, substitution and deletion rates. Unimpaired speech when combined with limited high-quality speech-impaired data improves performance of ASR systems in recognising severely impaired dysarthric speech. The C-MLLR adaptation technique was also found to be better than MLLR in recognising mildly and moderately impaired speech based on the statistical analysis of the WER. It was found that phoneme substitution was the biggest contributing factor in WER in dysarthric speech for all levels of severity. The results show that the speech acoustic models derived from suitable adaptation techniques improve the performance of ASR systems in recognising impaired speech with limited adaptation data.
Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia.
Mumtaz Begum
MB
Mustafa
Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia.
Siti Salwah
SS
Salim
Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia.
Noraini
N
Mohamed
Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia.
Bassam
B
Al-Qatab
School of Computer Engineering, Nanyang Technological University, Singapore, Singapore.
Chng Eng
CE
Siong
D003198:Computer Simulation; D016208:Databases, Factual; D004401:Dysarthria / Q000473:pathology / Q000628:therapy*; D006801:Humans; D016013:Likelihood Functions; D010700:Phonetics; D012720:Severity of Illness Index; D013061:Speech Acoustics; D049250:Speech Recognition Software