Selective Acoustic Feature Enhancement for Speech Emotion Recognition With Noisy Speech

Abstract

A <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">speech emotion recognition</i> (SER) system deployed on a real-world application is highly likely to encounter speech contaminated with unconstrained background noise. To deal with this issue, a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">speech enhancement</i> (SE) module can be attached to the SER system to compensate for the environmental difference of an input. Although the SE module can improve the quality and intelligibility of a given speech, there is a risk of affecting discriminative acoustic features for SER that are resilient to environmental differences. Exploring this idea, we propose to enhance only weak features that degrade the emotion recognition performance, while keeping strong features that are resilient to environmental differences. Our model first identifies weak feature sets by using multiple models trained with one acoustic feature at a time using clean speech. After training the single-feature models, we rank each speech feature by measuring three criteria: performance, robustness, and a joint rank ranking that combines performance and robustness. We group the weak features by cumulatively incrementing the features from the bottom to the top of each rank. Once the weak feature set is defined, we only enhance those weak features, keeping the resilient features unchanged. We implement these ideas with the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">low-level descriptors</i> (LLDs). We show that extracting LLDs from an enhanced speech signal does not improve the performance of weak features. Instead, directly enhancing the LLDs lead to better performance. Our experiment with clean and noisy versions of the MSP-Podcast corpus shows that the selective feature enhancement approach proposed in this study yields a 17.7% (arousal), 21.2% (dominance), and 3.3% (valence) performance gains over a system that enhances all the LLDs for the 10dB <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">signal-to-noise ratio</i> (SNR) condition.

Topics & Concepts