Chinese Speech Evaluation and Intelligent Training System Based on Dynamic Time Warping

Due to the differences in speech speed and intonation among different users, traditional speech recognition methods are difficult to accurately evaluate the user's speech, resulting in training failure. Therefore, this paper is dedicated to researching and developing a Chinese speech evaluation and intelligent training system based on Dynamic Time Warping (DTW). First, the overall design of the system is carried out to clarify the functional requirements of the system. Secondly, the speech signal is preprocessed, and the input speech signal is filtered, sampled, quantized, and other operations are performed. Then the speech signal is framed and windowed, and it is divided into multiple short-term stable speech frames. The Mel-frequency cepstral coefficient (MFCC) is used to extract feature parameters that can reflect the speech characteristics from the preprocessed speech frames. Then, the DTW algorithm is used to perform pattern matching on the extracted feature parameters, and the cumulative distance between the test speech sequence and the template speech sequence is calculated. The degree of similarity between the test speech and the template speech is judged by comparing the size of the cumulative distance. Subsequently, the evaluation module evaluates the speech quality of the test speech according to the cumulative distance output by the DTW algorithm processing module, and gives the corresponding evaluation results. Finally, the intelligent training module provides users with personalized training guidance based on the evaluation results and the training needs of users. The system's evaluation results were consistent with manual evaluations at an average of 97.4%, and users' MOS and STOI scores improved after training with the system. The results of this study can improve the voice quality of users and promote the popularization and application of voice interaction technology.