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 IDSPE-23.4
Paper Title Speech Emotion Recognition based on Listener Adaptive Models
Authors Atsushi Ando, Ryo Masumura, Hiroshi Sato, Takafumi Moriya, Takanori Ashihara, Yusuke Ijima, NTT Corporation, Japan; Tomoki Toda, Nagoya University, Japan
SessionSPE-23: Speech Emotion 1: Speech Emotion Recognition
LocationGather.Town
Session Time:Wednesday, 09 June, 15:30 - 16:15
Presentation Time:Wednesday, 09 June, 15:30 - 16:15
Presentation Poster
Topic Speech Processing: [SPE-ANLS] Speech Analysis
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract This paper presents a novel speech emotion recognition scheme that can deal with the individuality of emotion perception. Most conventional methods directly model the majority decision of multiple listener's perceived emotions. However, emotion perception varies with the listener, which means the conventional methods can mismatch the recognition results to human perception. In order to mitigate this problem, we propose a Listener Adaptive~(LA) model that reflects emotion recognition criteria of each listener. One-hot listener codes with several adaptation layers are employed in the LA model. The LA model yields the posterior probabilities of the listener-specific perceived emotions. Majority-voted emotion can be also estimated by averaging, in the LA model, the posterior probabilities for all listeners. Experiments on two emotional speech datasets demonstrate that the proposed approach offers improved listener-wise perceived emotion recognition performance in natural speech.