Speech Emotion Analysis in Noisy Real-World Environment

Automatic recognition of emotional states via speech signal has attracted increasing attention in recent years. A number of techniques have been proposed which are capable of providing reasonably high accuracy for controlled studio settings. However, their performance is considerably degraded when the speech signal is contaminated by noise. In this paper, we present a framework with adaptive noise cancellation as front end to speech emotion recognizer. We also introduce a new feature set based on cepstral analysis of pitch and energy contours. Experimental analysis shows promising results.