Multiresolution analysis (discrete wavelet transform) through Daubechies family for emotion recognition in speech
This work addresses emotion recognition in speech for applications like human-computer interaction, but it is incremental as it builds on existing wavelet methods.
The authors tackled emotion recognition in speech by applying multiresolution analysis with Daubechies wavelets to extract features from audio signals, achieving high accuracy in classifying seven emotional states without traditional frequency-time features.
We propose a study of the mathematical properties of voice as an audio signal. This work includes signals in which the channel conditions are not ideal for emotion recognition. Multiresolution analysis discrete wavelet transform was performed through the use of Daubechies Wavelet Family (Db1-Haar, Db 6, Db8, Db10) allowing the decomposition of the initial audio signal into sets of coefficients on which a set of features was extracted and analyzed statistically in order to differentiate emotional states. ANNs proved to be a system that allows an appropriate classification of such states. This study shows that the extracted features using wavelet decomposition are enough to analyze and extract emotional content in audio signals presenting a high accuracy rate in classification of emotional states without the need to use other kinds of classical frequency-time features. Accordingly, this paper seeks to characterize mathematically the six basic emotions in humans: boredom, disgust, happiness, anxiety, anger and sadness, also included the neutrality, for a total of seven states to identify.