Syllable based DNN-HMM Cantonese Speech to Text System
Timothy Wong Claire Li Sam Lam Billy Chiu Qin Lu Minglei Li Dan Xiong Roy Shing Yu Vincent T. Y. Ng
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.