End-to-End Amdo-Tibetan Speech Recognition Based on Knowledge Transfer

Abstract

The end-to-end speech recognition technology solves the problem that each component is independent and models cannot be jointly optimized in the traditional speech recognition model. It incorporates such components as the acoustic model, language model, and decoding unit of the hybrid model into a single neural network, that can avoid the inherent defects of multiple modules and greatly reduces the complexity of the speech recognition model. In this research, an Amdo-Tibetan speech recognition system is constructed based on Listen, Attend and Spell (LAS) model by the end-to-end speech recognition technology. It can realize the direct conversion from Amdo-Tibetan speech sequence to the corresponding character sequence and greatly reduces the difficulty of building the Amdo-Tibetan speech recognition model. To further improve the performance of the proposed system, the following improvements have been made: firstly, the Multi-Head attention mechanism is introduced to improve the alignment accuracy between state vectors of decoder and encoder; secondly, the label smoothing technique is adopted to solve the problem of over-fitting; thirdly, an N-gram language model is combined with the LAS model to increase the accuracy of speech recognition and the maximum mutual information (MMI) criterion is employed for discriminative training; and finally, transfer learning is utilized to overcome the problem of insufficient training data. Experimental results show that the proposed model can significantly enhance the performance of Amdo-Tibetan speech recognition.

Video surveillance using deep transfer learning and deep domain adaptation: Towards better generalization

“…To that end, the character error rate (CER) has been used instead of WER, although the evaluation principle remains the same. Besides, WER is also called phoneme error rate (PER) in schemes that adopt phoneme as a unit of measure rather than a word [53][54][55]. The word recognition rate (WRR) is a version of WER that may be used to assess ASR performance such that WRR = 1 − WER and N − (S + D) is the total number of successfully predicted words [25].…”

Using Hybrid Penalty and Gated Linear Units to Improve Wasserstein Generative Adversarial Networks for Single-Channel Speech Enhancement

“…In practical application scenarios, speech signals will inevitably be disturbed by many interference factors such as noise, echo, and reverberation. Therefore, speech enhancement technology has been widely used in household appliances, communications, speech recognition, automotive electronics, hearing aids, and other fields [1][2][3]. Traditional speech enhancement methods, based on signal processing and statistical modeling, have good performance for stationary noise.…”