Speech Emotion Recognition Using Fourier Parameters

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.

1 Introduction

Speech emotion recognition, which is defined as extracting the emotional states of a speaker from his or her speech, is attracting more attention. It is believed that speech emotion recognition can improve the performance of speech recognition systems [1] and is thus very helpful for criminal investigation, intelligent assistance [2], surveillance and detection of potentially hazardous events [3], and health care systems [4]. Speech emotion recognition is particularly useful in man-machine interaction [1], [6].