Neural network-based non-intrusive speech quality assessment using attention pooling function

EURASIP Journal on Audio, Speech, and Music Processing

Fang Liu¹  Weiming Yi¹  Jing Wang²  Miao Liu² 

[1] Key Laboratory of Language, Cognition and Computation Ministry of Industry and Information Technology, School of Foreign Languages, Beijing Institute of Technology;School of Information and Electronics, Beijing Institute of Technology;

关键词: Speech quality assessment;    Non-intrusive;    Neural network;    Attention pooling;    CNN-BLSTM;   

DOI  :  10.1186/s13636-021-00209-4

来源: DOAJ

【 摘 要 】

Abstract Recently, the non-intrusive speech quality assessment method has attracted a lot of attention since it does not require the original reference signals. At the same time, neural networks began to be applied to speech quality assessment and achieved good performance. To improve the performance of non-intrusive speech quality assessment, this paper proposes a neural network-based assessment method using attention pooling function. The proposed systems are based on the convolutional neural networks (CNNs), bidirectional long short-term memory (BLSTM), and CNN-LSTM structure. Comparing four types of pooling functions both theoretically and experimentally, we find the attention pooling function performs the best among the four. Experiments are conducted in a dataset containing various degraded speech signals with corresponding subjective quality scores. The results show that the proposed CNN-LSTM model using attention pooling function achieves state-of-the-art correlation coefficient (R) and root-mean-square error (RMSE) of 0.967 and 0.269, outperforming the performance of standardization ITU-T P.563 and autoencoder-support vector regression method.

【 授权许可】

Unknown