Automatically measuring speech fluency in people with aphasia: first achievements using read-speech data

ABSTRACT

Background

Speech and language pathologists (SLPs) often rely on judgements of speech fluency for diagnosing or monitoring patients with aphasia. However, such subjective methods have been criticised for their lack of reliability and their clinical cost in terms of time.

Aims

This study aims at assessing the relevance of a signal-processing algorithm, initially developed in the field of language acquisition, for the automatic measurement of speech fluency in people with aphasia (PWA).

Methods & Procedures

Twenty-nine PWA and five control participants were recruited via non-profit organizations and SLP networks. All participants were recorded while reading out loud a set of sentences taken from the French version of the Boston Diagnostic Aphasia Examination. Three trained SLPs assessed the fluency of each sentence on a five-point qualitative scale. A forward-backward divergence segmentation and a clustering algorithm were used to compute, for each sentence, four automatic predictors of speech fluency: pseudo-syllable rate, speech ratio, rate of silent breaks, and standard deviation of pseudo-syllable length. The four predictors were finally combined into multivariate regression models (a multiple linear regression — MLR, and two non-linear models) to predict the average SLP ratings of speech fluency, using a leave-one-speaker-out validation scheme.

Outcomes & Results

All models achieved accurate predictions of speech fluency ratings, with average root-mean-square errors as low as 0.5. The MLR yielded a correlation coefficient of 0.87 with reference ratings at the sentence level, and of 0.93 when aggregating the data for each participant. The inclusion of an additional predictor sensitive to repetitions improved further the predictions with a correlation coefficient of 0.91 at the sentence level, and of 0.96 at the participant level.

Conclusions

The algorithms used in this study can constitute a cost-effective and reliable tool for the assessment of the speech fluency of patients with aphasia in read-aloud tasks. Perspectives for the assessment of spontaneous speech are discussed.

KEYWORDS:

• speech fluency
• automatic assessment
• aphasia

Acknowledgments

The authors express their deep gratitude to all the participants who accepted being recorded, and to the three SLPs who participated in the fluency rating task. They also warmly thank Prof. Jean-Luc Nespoulous, Dr. Sébastien Déjean, and Dr. Saïd Jmel for their helpful advices. The study was funded by the European Regional Development Fund (ERDF), within the framework of the research project “Aphasie et Discours en Interaction (AADI) [Aphasia And Discourse in Interaction (AADI)]” (funding number: 2019-A03105-52). SMF also acknowledges support of Ramón y Cajal (RYC2020-028927-1), Ministerio de Ciencia e Innovación, Spain.

Declaration of interest statement

This study is part of the development, by Archean LABS (host institution of LF), of a software intended for speech-language therapists.

Table A2. Control participants

Notes

¹ https://aphasie.fr

² https://www.researchgate.net/project/Aphasia-and-interactive-discourse-analysis-creating-a-database-and-new-methods-for-exploiting-data-AADI

Additional information

Funding

This work was supported by the European Regional Development Fund [2019-A03105-52].