Speech Emotion Recognition Using Fourier Parameters

Kunxia Wang · Ning An · Bing Nan Li · Yanyong Zhang · Lian Li

409 citations0/0 files storedIEEE Transactions on Affective Computing

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Emotion and Mood Recognition Speech Recognition and Synthesis Speech and Audio Processing

Abstract

Recently, studies have been performed on harmony features for speech emotion recognition. It is found in our study that the first- and second-order differences of harmony features also play an important role in speech emotion recognition. Therefore, we propose a new Fourier parameter model using the perceptual content of voice quality and the first- and second-order differences for speaker-independent speech emotion recognition. Experimental results show that the proposed Fourier parameter (FP) features are effective in identifying various emotional states in speech signals. They improve the recognition rates over the methods using Mel frequency cepstral coefficient (MFCC) features by 16.2, 6.8 and 16.6 points on the German database (EMODB), Chinese language database (CASIA) and Chinese elderly emotion database (EESDB). In particular, when combining FP with MFCC, the recognition rates can be further improved on the aforementioned databases by 17.5, 10 and 10.5 points, respectively.

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References

2001Emotion recognition in human-computer interaction 2005A database of German emotional speech 2010Survey on speech emotion recognition: Features, classification schemes, and databases 2010Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications 2005Toward detecting emotions in spoken dialogs 2003The role of voice quality in communicating emotion, mood and attitude 2004Speech emotion recognition combining acoustic features and linguistic information in a hybrid support vector machine-belief network architecture 2001Nonlinear feature based classification of speech under stress

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2018Databases, features and classifiers for speech emotion recognition: a review 2019Speech Emotion Classification Using Attention-Based LSTM 2017Speech emotion recognition based on feature selection and extreme learning machine decision tree 2019Bagged support vector machines for emotion recognition from speech 2020MLT-DNet: Speech emotion recognition using 1D dilated CNN based on multi-learning trick approach

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