Improving Speech-Based Emotion Recognition by Using Psychoacoustic Modeling and Analysis-by-Synthesis

Most technical communication systems use speech compression codecs to save transmission bandwidth. A lot of development was made to guarantee a high speech intelligibility resulting in different compression techniques: Analysis-by-Synthesis, psychoacoustic modeling and a hybrid mode of both. Our first assumption is that the hybrid mode improves the speech intelligibility. But, enabling a natural spoken conversation also requires affective, namely emotional, information, contained in spoken language, to be intelligibly transmitted. Usually, compression methods are avoided for emotion recognition problems, as it is feared that compression degrades the acoustic characteristics needed for an accurate recognition [1]. By contrast, in our second assumption we state that the combination of psychoacoustic modeling and Analysis-by-Synthesis codecs could actually improve speech-based emotion recognition by removing certain parts of the acoustic signal that are considered “unnecessary”, while still containing the full emotional information. To test both assumptions, we conducted an ITU-recommended POLQA measuring as well as several emotion recognition experiments employing two different datasets to verify the generality of this assumption. We compared our results on the hybrid mode with Analysis-by-Synthesis-only and psychoacoustic modeling-only codecs. The hybrid mode does not show remarkable differences regarding the speech intelligibility, but it outperforms all other compression settings in the multi-class emotion recognition experiments and achieves even an \(\sim \)3.3% absolute higher performance than the uncompressed samples.