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

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Abstract

The work presented in this paper explores the effectiveness of incorporating the excitation source parameters such as strength of excitation and instantaneous fundamental frequency (\(F_0\)) for emotion recognition task from speech and electroglottographic (EGG) signals. The strength of excitation (SoE) is an important parameter indicating the pressure with which glottis closes at the glottal closure instants (GCIs). The SoE is computed by the popular zero frequency filtering (ZFF) method which accurately estimates the glottal signal characteristics by attenuating or removing the high frequency vocaltract interactions in speech. The arbitrary impulse sequence, obtained from the estimated GCIs, is used to derive the instantaneous \(F_0\). The SoE and the instantaneous \(F_0\) parameters are combined with the conventional mel frequency cepstral coefficients (MFCC) to improve the recognition rates of distinct emotions (Anger, Happy and Sad) using Gaussian mixture models as classifier. The performances of the proposed combination of SoE and instantaneous \(F_0\) and their dynamic features with MFCC coefficients are compared with the emotion utterances (4 emotions and neutral) from classical German full blown emotion speech database (EmoDb) having simultaneous speech and EGG signals and Surrey Audio Visual Expressed Emotion database (3 emotions and neutral) for both speaker dependent and speaker independent emotion recognition scenarios. To reinforce the effectiveness of the proposed features and for better statistical consistency of the emotion analysis, a fairly large emotion speech database of 220 utterances per emotion in Tamil language with simultaneous EGG recordings, is used in addition to EmoDb. The effectiveness of SoE and instantaneous \(F_0\) in characterizing different emotions is also confirmed by the improved emotion recognition performance in Tamil speech-EGG emotion database.

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Notes

  1. MFCC term used throughout this paper denotes 39 MFCC coefficients having 13 MFCC along with 13 velocity (\(\Delta\)) and 13 acceleration (\(\Delta\) \(\Delta\)) coefficients.

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Acknowledgements

Works carried out for this paper are funded by the completed DST-SERB project titled, “Analysis, Processing and Synthesis of Emotions in Speech (Ref No. SB/FTP/ETA-370/2012)”.

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  1. Center for Computational Engineering and Networking (CEN), Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Amrita University, Coimbatore, India

    D. Pravena & D. Govind

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  1. D. Pravena
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  2. D. Govind
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Correspondence to D. Govind.

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Pravena, D., Govind, D. Significance of incorporating excitation source parameters for improved emotion recognition from speech and electroglottographic signals. Int J Speech Technol 20, 787–797 (2017). https://doi.org/10.1007/s10772-017-9445-x

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