Earticle

초록

영어

This paper investigates the non-intrusive speech intelligibility estimation method in noise environments when the bottleneck feature of autoencoder is used as an input to a neural network. The bottleneck featurebased method has the problem of severe performance degradation when the noise environment is changed. In order to overcome this problem, we propose a novel non-intrusive speech intelligibility estimation method that adds the noise environment information along with bottleneck feature to the input of long short-term memory (LSTM) neural network whose output is a short-time objective intelligence (STOI) score that is a standard tool for measuring intrusive speech intelligibility with reference speech signals. From the experiments in various noise environments, the proposed method showed improved performance when the noise environment is same. In particular, the performance was significant improved compared to that of the conventional methods in different environments. Therefore, we can conclude that the method proposed in this paper can be successfully used for estimating non-intrusive speech intelligibility in various noise environments.

목차

Abstract
1. INTRODUCTION
2. NON-INTRUSIVE SPEECH INTELLIGIBILITY ESTIMATION BASED ON BOTTLENECK FEATURE WITH BACKGROUND NOISE INFORMATION
3. EXPERIMENTS AND RESULTS
4. CONCLUSION
ACKNOWLEDGEMENT
REFERENCES

키워드

저자

• Yue Ri Jeong [ Undergraduate Student, Dept. of Electronic and IT Media Engineering, Seoul National University of Science and Technology, Seoul, Korea ]
• Seung Ho Choi [ Professor, Dept. of Electronic and IT Media Engineering, Seoul National University of Science and Technology, Seoul, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Internet, Broadcasting and Communication

• 간기

  계간

• pISSN

  2288-4920

• eISSN

  2288-4939

• 수록기간

  2009~2025

• 십진분류

  KDC 326 DDC 380