Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/67831
Title: Syllable based DNN-HMM Cantonese speech to text system
Authors: Wong, TCT
Li, WYC
Chiu, WH
Lu, Q 
Li, M
Xiong, D
Yu, S
Ng, V 
Keywords: Cantonese speech recognition
DNN-HMM
Onset-Nucleus-Coda Syllable Scheme
Kaldi toolkit
Issue Date: 2016
Source: LREC 2016 : Tenth International Conference on Language Resources and Evaluation, European Language Resources Association, Portoroz, Slovenia, May 23-28, 2016 How to cite?
Abstract: This paper reports our work on building up a Cantonese Speech-to-Text (STT) system with a syllable based acoustic model. This is a part of an effort in building a STT system to aid dyslexic students who have cognitive deficiency in writing skills but have no problem expressing their ideas through speech. For Cantonese speech recognition, the basic unit of acoustic models can either be the conventional Initial-Final (IF) syllables, or the Onset-Nucleus-Coda (ONC) syllables where finals are further split into nucleus and coda to reflect the intra-syllable variations in Cantonese. By using the Kaldi toolkit, our system is trained using the stochastic gradient descent optimization model with the aid of GPUs for the hybrid Deep Neural Network and Hidden Markov Model (DNN-HMM) with and without I-vector based speaker adaptive training technique. The input features of the same Gaussian Mixture Model with speaker adaptive training (GMM-SAT) to DNN are used in all cases. Experiments show that the ONC-based syllable acoustic modeling with I-vector based DNN-HMM achieves the best performance with the word error rate (WER) of 9.66% and the real time factor (RTF) of 1.38812.
URI: http://hdl.handle.net/10397/67831
ISBN: 978-2-9517408-9-1 (electronic)
Appears in Collections:Conference Paper

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