Evaluation of syllable rate estimation in expressive speech and its contribution to emotion recognition

It is commonly accepted that speaking rate is an important aspect characterizing expressive speech. The speaking rate increases for emotions such as happiness and anger, and decreases for emotions such as sadness. In spite of these observations, most of the current speech emotion classifiers do not explicitly use speaking rate features. This study explores two interrelated questions to evaluate the role of speaking rate in emotion recognition: Can we reliably estimate syllable rate from emotional speech? Does syllable rate provide complementary emotional information over other acoustic features? We consider two syllable rate estimation algorithms, as well as reference values derived from forced alignment. We evaluate the performance of these syllable rate estimation methods in expressive speech (SEMAINE database). The analysis reveals a drop in performance as the intensity of the emotion increases. Next, we conduct emotion recognition experiments to evaluate the contribution of syllable rate in recognizing emotions. The emotion classification experiments demonstrate that features conveying accurate syllable rate estimations complement features that are commonly used in current emotion recognition system.