Abstract
Within the field of automatic speech recognition, the processing of dysarthric speech is a challenge because standard approaches are ineffective in presence of dysarthria. This paper presents preliminary evidence that the performance of speaker-dependent speech recognition systems trained for speakers with dysarthria may be substantially improved by tuning the size and shift of the spectral analysis window used to compute the initial short-time Fourier transform used in many speech front ends. Evidence for this comes from a set of experiments performed on a small collection of Italian speech (isolated words) from five different speakers suffering from different degrees of dysarthria. The experimental framework used in the paper constructs speaker-dependent GMM-HMM speech recognition models using the triphone Kaldi recipe and varying choices of the spectral analysis window size and shift. Results show a variable improvement (31% to 81%), according to the selected user with dysarthria.
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Marini, M., Meoni, G., Mulfari, D., Vanello, N., Fanucci, L. (2021). Enabling Smart Home Voice Control for Italian People with Dysarthria: Preliminary Analysis of Frame Rate Effect on Speech Recognition. In: Saponara, S., De Gloria, A. (eds) Applications in Electronics Pervading Industry, Environment and Society. ApplePies 2020. Lecture Notes in Electrical Engineering, vol 738. Springer, Cham. https://doi.org/10.1007/978-3-030-66729-0_13
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