Automatic Assessment of Speech Intelligibility for Individuals With Aphasia

作者：Le Duc; Licata Keli; Persad Carol; Provost Emily Mower

来源：IEEE/ACM Transactions on Audio Speech and Language Processing, 2016, 24(11): 2187-2199.

DOI：10.1109/TASLP.2016.2598428

• Aphasia
• apraxia
• acoustic modeling
• clinical application
• machine learning
• speech intelligibility assessment

Traditional in-person therapy may be difficult to access for individuals with aphasia due to the shortage of speech-language pathologists and high treatment cost. Computerized exercises offer a promising low-cost and constantly accessible supplement to in-person therapy. Unfortunately, the lack of feedback for verbal expression in existing programs hinders the applicability and effectiveness of this form of treatment. A prerequisite for producing meaningful feedback is speech intelligibility assessment. In this work, we investigate the feasibility of an automated system to assess three aspects of aphasic speech intelligibility: clarity, fluidity, and prosody. We introduce our aphasic speech corpus, which contains speech-based interaction between individuals with aphasia and a tablet-based application designed for therapeutic purposes. We present our method for eliciting reliable ground-truth labels for speech intelligibility based on the perceptual judgment of nonexpert human evaluators. We describe and analyze our feature set engineered for capturing pronunciation, rhythm, and intonation. We investigate the classification performance of our system under two conditions, one using human-labeled transcripts to drive feature extraction, and another using transcripts generated automatically. We show that some aspects of aphasic speech intelligibility can be estimated at human-level performance. Our results demonstrate the potential for the computerized treatment of aphasia and lay the groundwork for bridging the gap between human and automatic intelligibility assessment.

• 出版日期2016-11