Improving Automatic Emotion Recognition from speech using Rhythm and Temporal feature
This work addresses emotion recognition from speech, which is an incremental improvement for applications like human-computer interaction.
The paper tackled automatic emotion recognition from speech by incorporating rhythm and temporal features, achieving a recognition rate of 80.60% on the Berlin Emotion Database in a speaker-dependent framework.
This paper is devoted to improve automatic emotion recognition from speech by incorporating rhythm and temporal features. Research on automatic emotion recognition so far has mostly been based on applying features like MFCCs, pitch and energy or intensity. The idea focuses on borrowing rhythm features from linguistic and phonetic analysis and applying them to the speech signal on the basis of acoustic knowledge only. In addition to this we exploit a set of temporal and loudness features. A segmentation unit is employed in starting to separate the voiced/unvoiced and silence parts and features are explored on different segments. Thereafter different classifiers are used for classification. After selecting the top features using an IGR filter we are able to achieve a recognition rate of 80.60 % on the Berlin Emotion Database for the speaker dependent framework.