Acoustic adaptation in cross database audio visual SHMM training for phonetic spoken term detection
Kalantari, Shahram, Dean, David B., Sridharan, Sridha, Ghaemmaghami, Houman, & Fookes, Clinton B. (2015) Acoustic adaptation in cross database audio visual SHMM training for phonetic spoken term detection. In Proceedings of the Third Edition Workshop on Speech, Language and Audio in Multimedia, Association for Computing Machinery, Brisbane, Qld, pp. 11-14.
Visual information in the form of lip movements of the speaker has been shown to improve the performance of speech recognition and search applications. In our previous work, we proposed cross database training of synchronous hidden Markov models (SHMMs) to make use of external large and publicly available audio databases in addition to the relatively small given audio visual database. In this work, the cross database training approach is improved by performing an additional audio adaptation step, which enables audio visual SHMMs to benefit from audio observations of the external audio models before adding visual modality to them. The proposed approach outperforms the baseline cross database training approach in clean and noisy environments in terms of phone recognition accuracy as well as spoken term detection (STD) accuracy.
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|Item Type:||Conference Paper|
|Keywords:||audio visual spoken term detection, cross database training|
|Divisions:||Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Current > Research Centres > Smart Services CRC
|Copyright Owner:||Copyright 2015 ACM|
|Deposited On:||15 Sep 2015 23:49|
|Last Modified:||16 Dec 2015 01:43|
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