International Journal of Internet, Broadcasting and Communication

The Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회)
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Non-Intrusive Speech Intelligibility Estimation Using Autoencoder Features with Background Noise Information

  • Jeong, Yue Ri (Dept. of Electronic and IT Media Engineering, Seoul National University of Science and Technology) ;
  • Choi, Seung Ho (Dept. of Electronic and IT Media Engineering, Seoul National University of Science and Technology)
  • Received : 2020.07.21
  • Accepted : 2020.08.01
  • Published : 2020.08.31
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Abstract

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 feature-based 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.

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References

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