2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDAUD-14.4
Paper Title NON-INTRUSIVE BINAURAL PREDICTION OF SPEECH INTELLIGIBILITY BASED ON PHONEME CLASSIFICATION
Authors Jana Roßbach, Communication Acoustics and Cluster of Excellence Hearing4All, Carl-von-Ossietzky University Oldenburg, Germany; Saskia Röttges, Christopher F. Hauth, Thomas Brand, Medical Physics and Cluster of Excellence Hearing4All, Carl-von-Ossietzky University Oldenburg, Germany; Bernd T. Meyer, Communication Acoustics and Cluster of Excellence Hearing4All, Carl-von-Ossietzky University Oldenburg, Germany
SessionAUD-14: Quality and Intelligibility Measures
LocationGather.Town
Session Time:Wednesday, 09 June, 15:30 - 16:15
Presentation Time:Wednesday, 09 June, 15:30 - 16:15
Presentation Poster
Topic Audio and Acoustic Signal Processing: [AUD-QIM] Quality and Intelligibility Measures
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract In this study, we explore an approach for modeling speech intelligibility in spatial acoustic scenes. To this end, we combine a non-intrusive binaural frontend with a deep neural network (DNN) borrowed from a standard automatic speech recognition (ASR) system. The DNN estimates phoneme probabilities that degrade in the presence of noise and reverberation, which is quantified with an entropy-based measure. The model output is used to predict speech recognition thresholds, i.e., signal-to-noise ratio with 50\% word recognition accuracy. It is compared to measured data obtained from eight normal-hearing listeners in acoustic scenarios with varying positions of localized maskers, different rooms and reverberation times. The model is non-intrusive; yet it produces a root mean squared error in the range of 0.6-2.1\,dB, which is similar to results obtained with a reference model (0.3-1.8\,dB) that uses oracle knowledge both in the frontend and in the backend stage.