Dysarthric speech classification from coded telephone speech using glottal features

Abstract

This paper proposes a new dysarthric speech classification method from coded telephone speech using glottal features. The proposed method utilizes glottal features, which are efficiently estimated from coded telephone speech using a recently proposed deep neural net-based glottal inverse filtering method. Two sets of glottal features were considered: (1) time-and frequency-domain parameters and (2) parameters based on principal component analysis (PCA). In addition, acoustic features are extracted from coded telephone speech using the openSMILE toolkit. The proposed method utilizes both acoustic and glottal features extracted from coded speech utterances and their corresponding dysarthric/healthy labels to train support vector machine classifiers. Separate classifiers are trained using both individual, and the combination of glottal and acoustic features. The coded telephone speech used in the experiments is generated using the adaptive multi-rate codec, which operates in two transmission bandwidths: narrowband (300 Hz -3.4 kHz) and wideband (50 Hz -7 kHz). The experiments were conducted using dysarthric and healthy speech utterances of the TORGO and universal access speech (UA-Speech) databases. Classification accuracy results indicated the effectiveness of glottal features in the identification of dysarthria from coded telephone speech. The results also showed that the glottal features in combination with the openSMILE-based acoustic features resulted in improved classification accuracies, which validate the complementary nature of glottal features. The proposed dysarthric speech classification method can potentially be employed in telemonitoring application for identifying the presence of dysarthria from coded telephone speech.

Ensemble Learning of Hybrid Acoustic Features for Speech Emotion Recognition

“…Narendra and Alku [38] proposed a new dysarthric speech classification method from a coded telephone speech using glottal features with a DNN based glottal inverse filtering method. They considered two sets of glottal features based on time and frequency domain parameters plus parameters based on principal component analysis (PCA).…”

Detection of Speech Impairments Using Cepstrum, Auditory Spectrogram and Wavelet Time Scattering Domain Features

“…Current research in the computer science field focuses on replicating the analysis of SLP with assistive devices [8]- [11], adapting heuristic algorithms [12], [13] and deep learning [14]- [16] for monitoring change in speech patterns, speech recognition and classification [17]- [20]. In addition, wavelet transforms (discrete, continuous, tunable-Q) are successfully utilized for speech impairment monitoring based on voice signal analysis [21], [22].…”

An Appraisal on Speech and Emotion Recognition Technologies based on Machine Learning

“…A conversation is being prepared to become an evolving technology that enables individuals to communicate with machines. From this point forward, it was possible to develop speech recognition software [12][13][14][15][16][17]. In 1922, the major production began.…”