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.

通过使用基于音节的声学模型构建广东话语音转文本系统，该研究旨在帮助有书写困难的阅读障碍学生通过语音表达思想，实验结果表明基于 ONC 的音节声学建模在识别准确率和实时因子方面达到最佳性能。

基于音节的 DNN-HMM 粤语语音到文本系统

Syllable based DNN-HMM Cantonese Speech to Text System

The Kaldi toolkit is becoming popular for constructing automated speech
recognition (ASR) systems. Meanwhile, in recent years, deep neural networks
(DNNs) have shown state-of-the-art performance on various ASR tasks. This
document describes our open-source recipes to implement fully-fledged DNN
acoustic modeling using Kaldi and PDNN. PDNN is a lightweight deep learning
toolkit developed under the Theano environment. Using these recipes, we can
build up multiple systems including DNN hybrid systems, convolutional neural
network (CNN) systems and bottleneck feature systems. These recipes are
directly based on the Kaldi Switchboard 110-hour setup. However, adapting them
to new datasets is easy to achieve.

本文介绍了使用 Kaldi 套件和 PDNN，使用 DNN 声学建模实现全面操作的一些开源配方，可以构建多种语音识别系统，包括 DNN 混合系统、卷积神经网络系统和瓶颈特征系统。