DESCU: Dyadic emotional speech corpus and recognition system for Urdu language

Speech signal contains the emotional state of a speaker along with the message. The recognition of the emotional state of a speaker helps in determining the true meaning of a message and allows for more natural communication between humans and machines. This paper presents the design and development of a dyadic emotional speech corpus for the Urdu language. The corpus is developed by recording dialog scenarios for anger, happy, neutral, and sad emotions. The performance of frame-level features, utterance -level features, and spectrograms have been evaluated in this work. Emotion recognition experiments have been conducted using classifiers including Support Vector Machine, Hidden Markov Models and Convolutional Neural Networks. Experimental results show that the utterance-level features outperform the frame-level features and spectrograms. The combined feature set of cepstral, spectral, prosodic, and voice quality features performs better than the individual feature sets. The unweighted average recalls of 84.1%, 80.2%, 84.7% have been achieved for speaker-dependent and speaker-independent and text-independent emotion recognition, respectively.

Automated summary

This paper presents the design and development of a dyadic emotional speech corpus for the Urdu language. Emotion recognition experiments have been conducted using various classifiers, and the utterance-level features have shown better performance than frame-level features and spectrograms. The combined feature set of cepstral, spectral, prosodic, and voice quality features performs better than individual feature sets, achieving high recall rates for emotion recognition.