A Machine Learning Assessment System for Spoken English Based on Linear Predictive Coding

Abstract

In the teaching of English, there is an increasing focus on practical communication skills. As a result, the speaking test component has received more and more attention from education experts. With the rapid development of modern computer technology and network technology, the use of computers to assess the quality of spoken English has become a hot topic of research in related fields at present. A machine learning assessment system based on linear predictive coding is proposed in order to achieve automatic scoring of spoken English tests. First, the principle of linear predictive coding and decoding is analyzed, and the traditional linear predictive coding and decoding algorithm is improved by using hybrid excitation instead of the traditional binary excitation. Second, the overall structure of the machine learning assessment system is designed, which mainly includes division into four modules: acoustic model acquisition module, speech recognition module, standard pronunciation transcription module, and decision module. Then, the speech recognition module is implemented by an improved linear predictive speech coding method to acquire the feature parameters of the speech signal and generate the speech feature vector. Finally, the convolutional neural network algorithm is used to train the speech features so as to implement the acoustic model acquisition module. The experimental results show that the improved linear predictive speech coding method yields more natural and higher intelligibility speech signals. The designed machine learning evaluation system is able to accurately detect information about the quality of the learner’s pronunciation.

English Speaking Assessment Algorithm Based on Deep Learning

“…Wang [22] has presented ML-AS-SE depend on LPC. Here, analyses the idea of linear predictive coding, decoding, suggests employing hybrid excitation rather than binary excitation to improve existing algorithm.…”

Mel Frequency Cepstral Coefficients (MFCC) Method and Multiple Adaline Neural Network Model for Speaker Identification

“…Much research has been done with themes related to voice identification using artificial neural network methods, Self-Organizing Maps (SOM), Backpropagation, and other rules [7]. There are several speech features commonly used to extract speaker characteristics, including Linear predictive coding (LPC), Mel-Frequency Cepstrum Coefficients (MFCC), and Lateral Prefrontal Cortex (LPFC) [8]- [11]. MFCC has good results for feature extraction in sound and images [12].…”