Comparing Supervised Models and Learned Speech Representations for Classifying Intelligibility of Disordered Speech on Selected Phrases

• DOI: 10.21437/interspeech.2021-1913
• Publication Date: 2021-08-27T05:59:39Z
• Authors: Subhashini Venugo...; Joel Shor; Manoj Plakal; Jimmy Tobin; Katrin Tomanek; Jordan R. Green; Michael P. Brenner
• Keywords: Intelligibility (philosophy); Phrase

Automatic classification of disordered speech can provide an objective tool for identifying the presence and severity impairment. Classification approaches also help identify hard-to-recognize samples to teach ASR systems about variable manifestations impaired speech. Here, we develop compare different deep learning techniques classify intelligibility on selected phrases. We collected from a diverse set 661 speakers with variety self-reported disorders speaking 29 words or phrases, which were rated by speech-language pathologists their overall using five-point Likert scale. then evaluated classifiers developed 3 approaches: (1) convolutional neural network (CNN) trained task, (2) non-semantic representations CNNs that used unsupervised [1], (3) acoustic (encoder) embeddings system typical [2]. found encoder's considerably outperform other two detecting classifying Further analysis shows cluster spoken phrase, while speaker. Also, longer phrases are more indicative deficits than single words.