Optimising Speaker-Dependent Feature Extraction Parameters to Improve Automatic Speech Recognition Performance for People with Dysarthria

Abstract

Within the field of Automatic Speech Recognition (ASR) systems, facing impaired speech is a big challenge because standard approaches are ineffective in the presence of dysarthria. The first aim of our work is to confirm the effectiveness of a new speech analysis technique for speakers with dysarthria. This new approach exploits the fine-tuning of the size and shift parameters of the spectral analysis window used to compute the initial short-time Fourier transform, to improve the performance of a speaker-dependent ASR system. The second aim is to define if there exists a correlation among the speaker’s voice features and the optimal window and shift parameters that minimises the error of an ASR system, for that specific speaker. For our experiments, we used both impaired and unimpaired Italian speech. Specifically, we used 30 speakers with dysarthria from the IDEA database and 10 professional speakers from the CLIPS database. Both databases are freely available. The results confirm that, if a standard ASR system performs poorly with a speaker with dysarthria, it can be improved by using the new speech analysis. Otherwise, the new approach is ineffective in cases of unimpaired and low impaired speech. Furthermore, there exists a correlation between some speaker’s voice features and their optimal parameters.

Pareto-Optimized Non-Negative Matrix Factorization Approach to the Cleaning of Alaryngeal Speech Signals

“…Fu et al [58] created a Sch-net neural network built on a convolutional neural network for end-to-end schizophrenia speech identification using deep learning techniques, implying that it has the potential to help in the diagnosis of a particular language disability. Marini et al [59] verified the efficacy of a speech analysis approach for dysarthria speakers by modifying the size and shift parameters of the spectral analysis window to increase ASR system performance.…”

Improved Feature Parameter Extraction from Speech Signals Using Machine Learning Algorithm

“…Feature extraction reduces the magnitude of the speech signal, devoid of causing any damage to the power of the speech signal [ 15 , 16 , 17 ]. In [ 18 ], the authors introduced a new approach that exploits the fine-tuning of the size and shift parameters of the spectral analysis window used to compute the initial short-time Fourier transform to improve the performance of a speaker-dependent automatic speech recognition (ASR) system. In the absence of doctors, laypeople can employ decision assistance systems that were created using artificial intelligence approaches.…”