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To improve the performance of multi-accent Mandarin speech recognition task,this paper presented a method for hybrid end-to-end automatic speech recognition(ASR) by combining CTC and multi-head attention by using a multiobjective training and joint decoding.The analysis shows that hybrid model with lower CTC weight and deeper encoder layers performance better learning capacity.And it trained a very deep models with up to 48 layers for encode-decoder architecture,which outperforms all previous end-to-end ASR approaches on Aidatatang 200 h multi-accent dataset,achieves 5.6% character error rate(CER) and 26.2% sentence error rate(SER).The experiment proves that the recognition rate of the end-to-end model proposed exceeds the general end-to-end model,and it has certain advancedness in solving the Mandarin recognition with accents. [ABSTRACT FROM AUTHOR]

In the era of big data, many of traditional machine learning methods of disposing unlabeled raw voice data have become less applicable. At the same time, deep learning models can directly process unlabeled data because of its powerful capability of modeling to deal with the massive data, and has become a hot research in the field of speech recognition. To begin with, this paper analyzed and summarized the state-of-the-art deep learning of models. And then,it discussed the applications to speech recognition with speech features extraction and acoustic modeling. Finally,it concluded the problems faced and development orientation. [ABSTRACT FROM AUTHOR]

To improve the performance of the conventional speech recognition system, this paper introduced the autoencoder deep learning neural networks which was applied to speech recognition. The neural networks based on deep learning introduced greedy layer-wise learning algorithm by pretraining and fine-tuning. It could extract the essential features of speech signal which was needed to recognition. It could overcome the shortcomings of the conventional multilayer artificial neural networks which easily trapped into local optimum when training the model. And they needed a large number of labeled data. Then the structured alignment networks could align arbitrary frames of features to fixed frames. And it input these features to a classifier to speech recognition. This paper did some experiment with back propagation neural networks and autoencoder neural networks respectively. The results illustrate that the deep learning neural networks can outperform the conventional neural networks by 20.0% in accuracy. It is an excellent speech recognition model. [ABSTRACT FROM AUTHOR]