Research on automatic speech recognition based on a DL–T and transfer learning

Speech has been a natural and effective way of communication, widely used in the field of information-communication and human–machine interaction. In recent years, various algorithms have been used for achieving efficient communication. The main purpose of automatic speech recognition (ASR), one of the key technologies in this field, is to convert the analog signals of input speech into corresponding text digital signals. Further, ASR can be divided into two categories: one based on hidden Markov model (HMM) and the other based on end to end (E2E) models. Compared with the former, E2E models have a simple modeling process and an easy training model and thus, research is carried out in the direction of developing E2E models for effectively using in ASR. However, HMM-based speech recognition technologies have some disadvantages in terms of prediction error rate, generalization ability, and convergence speed. Therefore, recurrent neural network–transducer (RNN–T), a typical E2E acoustic model that can model the dependencies between the outputs and can be optimized jointly with a Language Model (LM), was proposed in this study. Further, a new acoustic model of DL–T based on DenseNet (dense convolutional network)–LSTM (long short-term memory)–Transducer, was proposed to solve the problems of a high prediction error rate and slow convergence speed in a RNN–T. First, a RNN–T was briefly introduced. Then, combining the merits of both DenseNet and LSTM, a novel acoustic model of DL–T, was proposed in this study. A DL–T can extract high-dimensional speech features and alleviate gradient problems and it has the advantages of low character error rate (CER) and fast convergence speed. Apart from that, a transfer learning method suitable for a DL–T was also proposed. Finally, a DL–T was researched in speech recognition based on the Aishell–1 dataset for validating the abovementioned methods. The experimental results show that the relative CER of DL–T is reduced by 12.52% compared with RNN–T, and the final CER is 10.34%, which also demonstrates a low CER and better convergence speed of the DL–T.