Automatic recognition and detection of aphasic natural speech

TL;DR

This study demonstrates that automatic speech recognition and natural speech feature analysis can effectively and efficiently detect aphasia in stroke patients, improving clinical assessment methods.

Contribution

It introduces a semi-automatic pipeline combining ASR and speech features for aphasia detection, outperforming previous models in accuracy.

Findings

ASR achieved a 24.5% WER on aphasic speech.

SVM classifier reached 86.6% accuracy in detecting aphasia.

Fluency features were most influential in classification.

Abstract

Aphasia is a language disorder affecting one third of stroke patients. Current aphasia assessment does not consider natural speech due to the time consuming nature of manual transcriptions and a lack of knowledge on how to analyze such data. Here, we evaluate the potential of automatic speech recognition (ASR) to transcribe Dutch aphasic speech and the ability of natural speech features to detect aphasia. A picture-description task was administered and automatically transcribed in 62 persons with aphasia and 57 controls. Acoustic and linguistic features were semi-automatically extracted and provided as input to a support vector machine (SVM) classifier. Our ASR model obtained a WER of 24.5%, outperforming earlier ASR models for aphasia. The SVM shows high accuracy (86.6%) at the individual level, with fluency features as most dominant to detect aphasia. ASR and semi-automatic feature extraction can thus facilitate natural speech analysis in a time efficient manner in clinical practice.