On the use of voice activity detection in speech emotion recognition

Abstract

Emotion recognition through speech has many potential applications, however the challenge comes from achieving a high emotion recognition while using limited resources or interference such as noise. In this paper we have explored the possibility of improving speech emotion recognition by utilizing the voice activity detection (VAD) concept. The emotional voice data from the Berlin Emotion Database (EMO-DB) and a custom-made database LQ Audio Dataset are firstly preprocessed by VAD before feature extraction. The features are then passed to the deep neural network for classification. In this paper, we have chosen MFCC to be the sole determinant feature. From the results obtained using VAD and without, we have found that the VAD improved the recognition rate of 5 emotions (happy, angry, sad, fear, and neutral) by 3.7% when recognizing clean signals, while the effect of using VAD when training a network with both clean and noisy signals improved our previous results by 50%.

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“…VAD allows the detection of speech regions in a given audio recording. Many studies [27,5,10] have shown that the application of voice activity detection in speech-analysis systems can produce cleaner data and achieve a higher performance. We implement this using Pyannote.audio, an open-source collection of neural building blocks for speaker diarization [9].…”