Semi-Supervised Acoustic Model Training by Discriminative Data Selection from Multiple ASR Systems' Hypotheses

While the performance of ASR systems depends on the size of the training data, it is very costly to prepare accurate and faithful transcripts. In this paper, we investigate a semisupervised training scheme, which takes the advantage of huge quantities of unlabeled video lecture archive, particularly for the deep neural network (DNN) acoustic model. In the proposed method, we obtain ASR hypotheses by complementary GMM-and DNN-based ASR systems. Then, a set of CRF-based classifiers is trained to select the correct hypotheses and verify the selected data. The proposed hypothesis combination shows higher quality compared with the conventional system combination method (ROVER). Moreover, compared with the conventional data selection based on confidence measure score, our method is demonstrated more effective for filtering usable data. Significant improvement in the ASR accuracy is achieved over the baseline system and in comparison with the models trained with the conventional system combination and data selection methods

Corpus and transcription system of Chinese Lecture Room

The paper introduces our project on automatic speech recognition (ASR) of Chinese lectures. For a comprehensive study on spontaneous Chinese, we compile a corpus of Chinese Lecture Room (CCLR), which has faithful transcripts and caption texts. Based on the annotated alignment of these texts, we conduct analysis on linguistic phenomena of spontaneous Chinese speech. We also develop a baseline ASR system with this corpus, and refine it with the DNN-HMM framework. By exploiting the lecture data without faithful transcripts and conducting unsupervised speaker adaptation, significant improvement of ASR accuracy is achieved. Index Terms: speech recognition, acoustic model, lectur