Title: Voice analysis rehabilitation platform based on LSTM algorithm

Authors: Alessandro Massaro; Giacomo Meuli; Nicola Savino; Angelo Maurizio Galiano

Addresses: Dyrecta Lab Srl, Via Vescovo Simplicio, N. 45, 70014 Conversano, BA, Italy ' Dyrecta Lab Srl, Via Vescovo Simplicio, N. 45, 70014 Conversano, BA, Italy ' Dyrecta Lab Srl, Via Vescovo Simplicio, N. 45, 70014 Conversano, BA, Italy ' Dyrecta Lab Srl, Via Vescovo Simplicio, N. 45, 70014 Conversano, BA, Italy

Abstract: The proposed work discusses the results of a research project based on the recognition of correctly pronounced words and phrases by implementing a web platform implementing an acoustic training model. The acoustic training model is performed by a long short-term memory - LSTM - algorithm, able to recognise the speech disorder by assigning a score for each test type. The paper discusses the platform design and implementation. The tests are performed for different kind of exercises in rehabilitation patterns. The adopted approach is based on the formulation of acoustic model integrating a training dictionary of correct phonemes to pronounce. The platform enables a real time automatic score of the performed exercises and the test planning. The LSTM training dataset can be enriched by adding new exercise to learn. The output graphical dashboards enforce clinical evaluations and reporting.

Keywords: speech disorder recognition; long short-term memory; LSTM; telemedicine platform.

DOI: 10.1504/IJTMCP.2022.123138

International Journal of Telemedicine and Clinical Practices, 2022 Vol.3 No.4, pp.327 - 340

Received: 22 Apr 2020
Accepted: 15 Aug 2020
Published online: 31 May 2022 *