收稿日期: 2014-02-27
修回日期: 2014-03-07
网络出版日期: 2015-01-15
基金资助
国家自然科学基金(61005019,61273268,61370034,90920302)和北京市自然科学基金(KZ201110005005)资助
Bottleneck features and subspace Gaussian mixture models for low-resource speech recognition
Received date: 2014-02-27
Revised date: 2014-03-07
Online published: 2015-01-15
吴蔚澜 , 蔡猛 , 田垚 , 杨晓昊 , 陈振锋 , 刘加 , 夏善红 . 低数据资源条件下基于Bottleneck特征与SGMM模型的语音识别系统[J]. 中国科学院大学学报, 2015 , 32(1) : 97 -102 . DOI: 10.7523/j.issn.2095-6134.2015.01.016
State-of-the-art speech recognition systems often depend on a lot of training data, but perform poorly when limited data is available. In this paper, we study speech recognition systems under low-resource condition. The subspace Gaussian mixture (SGMM) model is first applied to reduce the number of parameters. The model is further enhanced by discriminative training based on maximum mutual information criterion. The bottleneck features based on deep neural networks are then studied to make robust feature extraction. The SGMM model and the bottleneck features are finally combined to produce a novel speech recognition system under low-resource condition. On the standard OpenKWS 2013 evaluation corpus, experimental results show the combination of the two technologies brings substantial relative improvement of about 12% over the baseline system.
Key words： speech recognition; low-resource; acoustic model; acoustic feature
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