Noisy speech training in MFCC-based speech recognition with noise suppression toward robot assisted autism therapy

Conference proceedings article

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Publication Details

Author list: Attawibulkul S., Kaewkamnerdpong B., Miyanaga Y.

Publisher: Hindawi

Publication year: 2017

Volume number: 2017-January

Start page: 1

End page: 5

Number of pages: 5

ISBN: 9781538608821

ISSN: 0146-9428

eISSN: 1745-4557

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046351484&doi=10.1109%2fBMEiCON.2017.8229135&partnerID=40&md5=3ea465741dcd10ce80f48a2324343224

Languages: English-Great Britain (EN-GB)

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Abstract

Autism Spectrum Disorder (ASD) is a group of neurodevelopmental disorders. Autistic children experience challenges in three important areas: social communication, social interaction and repetitive behavior. Robots have become tools to support therapists in autism therapy. Toward integrating social interaction and communication in robot-assisted autism therapy, the robot should have speech recognition ability that can be used in noisy environment. This study investigated the potential of using noisy speech training in MFCC-based speech recognition system with noise suppression toward robot-assisted autism therapy. Experimental results with clean speech training on Japanese speech database suggested that MFCC with noise suppression technique could provide the improvement with significantly higher recognition accuracy than MFCC. With noisy speech training with noise type found in the environment, the performance can be improved even more. ฉ 2017 IEEE.

Keywords

Mel frequency cepstral coefficients, Noisy speech training, Robot-assisted autism therapy