Reducing Speech Noise for Patients with Dysarthria in Noisy Environments

Abstract

SUMMARYDysarthric speech results from damage to the central nervous system involving the articulator, which can mainly be characterized by poor articulation due to irregular sub-glottal pressure, loudness bursts, phoneme elongation, and unexpected pauses during utterances. Since dysarthric speakers have physical disabilities due to the impairment of their nervous system, they cannot easily control electronic devices. For this reason, automatic speech recognition (ASR) can be a convenient interface for dysarthric speakers to control electronic devices. However, the performance of dysarthric ASR severely degrades when there is background noise. Thus, in this paper, we propose a noise reduction method that improves the performance of dysarthric ASR. The proposed method selectively applies either a Wiener filtering algorithm or a Kalman filtering algorithm according to the result of voiced or unvoiced classification. Then, the performance of the proposed method is compared to a conventional Wiener filtering method in terms of ASR accuracy.

Combining degradations: The effect of background noise on intelligibility of disordered speech

“…Given that a primary concern of listeners with hearing loss is a difficulty understanding speech in noise, much work has gone in to the development of hearing aid technology to improve SNR and increase speech intelligibility (e.g., Healy et al, 2013). However, studies with regard to the effect of source degradations on these noise reduction technologies are essential, particularly as the identification and segregation of disordered speech is likely much more challenging than for typical speech (Seong et al, 2014). Such studies would also address issues of ecological validity and clinical application, given that conditions such as presbycusis (age-related sensorineural hearing loss) and dysarthria generally occur in older adulthood.…”