## 2 Relate Work
### Related Work in the ASR System  
ASR, which can convert the voice directly input by human beings into machine-recognizable text, can be output into statements by specific meaning through certain natural language processing. The narrowly understood speech recognition is speech recognition and conversion into text (Speech-To-Text), which corresponds to Text-To-Speech. Speech recognition technology has mainly experienced three eras: the Gaussian Mixed Model-Hidden Markov Model (GMM-HMM) era, the Deep Neural Networks (DNN)-HMM era, and the End-To-End era. In the early development of speech recognition, people used traditional template matching technology, and the HMM gradually became the mainstream after the start of the research. [PERSON] et al. [1] linked speech signal modeling with the HMM, and pointed out that the HMM is particularly suitable for the recognition of isolated words, so the results are better in isolated words and non-specific speech recognition applications. [PERSON] et al. [2] first extended the HMM to phone recognition that has nothing to do with the speaker, using linear-predictive-coding (LPC) parameters and discrete HMMs to obtain 73.80% accuracy when training on the DARPA TIMIT Acoustic-Phonetic Continuous SpeechCorpus (TIMIT) database, and obtained the best result of the database data evaluation at that time. It provides a good foundation for continuous speech recognition (LVCSR), with a large vocabulary regardless of the speaker. With the introduction of Expectation Maximization (EM), the richness of the GMM can be used to express the relationship between the HMM status and voice input, which provides the possibility for the actual development of a speech recognition system. In the research of [PERSON] et al. [3], GMM-HMM can more effectively capture the changes of sound track caused by speaker-related factors. Therefore, the experimental results from the TIMIT database show that GMM-HMM has better performance than the single HMM model. Since then, the artificial neural network (ANN) has been introduced into the field of speech recognition. However, because the neural network used at that time was only a shallow layer, its performance was not as good as GMM-HMM. The years to come will still be the era of the GMM-HMM framework, because the development of speech recognition systems has been slow in research and application, and has not reached the practical level; therefore, the development of speech recognition has fallen into a bottleneck. As the development of the DNN promotes the progress of speech recognition, the HMM also performs well in the combined application of the DNN. [PERSON] et al. [4] combined the deep trust network (DBN) with the HMM, and studied two DBN-based acoustic models: backpropagation DBN (BP-DBN), and associative storage DBN (AM-DBN), which were superior to other techniques in the training corpus TIMIT and achieved the best results at that time. The DNN was applied to acoustic modeling on speech recognition. After training with the TIMIT, the word error rate was significantly reduced, and when faced with a challenging, large vocabulary and spontaneous speech dataset, the performance was significantly higher than GMM-HMM, and the absolute sentence recognition accuracy was greatly improved and the relative error rate was significantly reduced. This was a major breakthrough in speech recognition based on deep neural network acoustic models. Since then, the wave of deep learning in the field of speech recognition has been pulled, and speech recognition has opened the DNN-HMM era. In the era of DNN-HMM, scholars combine different DNN models with the HMM. [PERSON] et al. [5] further improved the effect of speech recognition and developed the free open source package Kaldi, which was the first to integrate various speech recognition models, including the classic hidden Markov model (HMM) and various deep neural networks. In addition, the trial of the RNN in speech recognition continues.