Significance of incorporating excitation source parameters for improved emotion recognition from speech and electroglottographic signals

Excitation Features of Speech for Emotion Recognition Using Neutral Speech as Reference

“…To measure the changes in the closed to open phase regions of the glottis, a ratio between the high-frequency and the low-frequency spectral energies was proposed in [36]. In [15,16,27,41,42], some of these excitation features were used to study emotions in speech. In [16,27], the authors analyzed excitation features [instantaneous fundamental frequency (F 0 ), strength of excitation (SoE), energy of excitation (EoE) [16] and loudness (η)], which are extracted at the sub-segmental level of speech, for four emotions (anger, happiness, sadness and neutral state).…”

“…The SoE parameter and the ratio of spectral energies between the high-frequency and the low-frequency ranges were used for discriminating angry and happy speech in [15]. In [41,42], features such as F 0 and SoE, and their first and second derivatives were used for the analysis and discrimination of emotions. In addition, in [18,41,44] the effect of emotions on the excitation of speech production was studied using prosody modification by converting speech in one emotion to another.…”

“…In [21,22,36,38,39,62], methods were developed to derive excitation features from the speech signal. In [15,16,18,27,41,41,42,44], the authors analyzed excitation features [instantaneous fundamental frequency (F 0 ), strength of excitation (SoE), energy of excitation (EoE) and loudness (η)], which are extracted at the sub-segmental level of speech, for four emotions (anger, happiness, sadness and neutral state). Motivated by the good results achieved in [15,16,18,27,41,42,44], where it was shown that excitation features capture significant information about emotions, we make a systematic investigation in the analysis of features for two databases (including cases where lexical content is both same and different) and develop an emotion recognition system using neutral speech features as reference.…”

“…In [15,16,18,27,41,41,42,44], the authors analyzed excitation features [instantaneous fundamental frequency (F 0 ), strength of excitation (SoE), energy of excitation (EoE) and loudness (η)], which are extracted at the sub-segmental level of speech, for four emotions (anger, happiness, sadness and neutral state). Motivated by the good results achieved in [15,16,18,27,41,42,44], where it was shown that excitation features capture significant information about emotions, we make a systematic investigation in the analysis of features for two databases (including cases where lexical content is both same and different) and develop an emotion recognition system using neutral speech features as reference. This recognition system is developed based on the observation made in the feature analysis part of the study, which highlighted deviations in emotional features in a two-dimensional feature space compared to the two-dimensional feature space of neutral speech.…”

Design of the Speech Emotion Recognition Model

“…In 2017, Gangamohan et al achieved identification rates of 76 and 69% on the IITH-H and EMO-DB databases, respectively, by calculating the Kullback-Leibler (KL) distance of the excitation source signals [6]. In 2019, Pravena and Govind determined the intensity of the excitation source and the base frequency of the speech signal and calculated its statistical properties using the Gaussian Mixture Model (GMM), which further improved the efficiency of the identification of these excitation source features [7].…”