The application of machine learning algorithms in speech recognition error detection

Abstract

The field of speech recognition technology has experienced a significant progress in recent years, with error detection being a crucial research domain. The present study offers a comprehensive overview of this area. Prior to the emergence of neural networks, Hidden Markov models (HMM) have been widely employed as a primary framework for speech recognition. However, this model only achieves local optimality, leading to its gradual replacement by neural network models, which have attracted considerable attention from researchers aiming to enhance their recognition performance. Various models, such as variant RNNs, bidirectional recurrent neural networks, and Fastcorrect2 have been developed. This paper introduces HMM, followed by a presentation of several neural network models, which entail a detailed description of their respective framework, principle, and idea. The variant RNN model is designed to enhance the recursive connection between input and output layers, while the deep bidirectional recurrent neural network model simulates the nonlinear relationship between input feature vectors and output labels using two models, namely bidirectional and deep. Additionally, the FastCorrect2 model enhances the voting effect of candidate words and the alignment algorithm. Finally, the study highlights the application of speech recognition error detection in everyday life, emphasizing the importance of speech recognition.