Sad state analysis of speech signals using different clustering algorithm

The objective of this paper is to analyse the sad state of speech emotion using voice quality features. This will help the family members, relatives, well-wishers and medical practitioners for timely action to the needy person before onset of deep depression that may danger his/her life. Fuzzy C-means and K-means clustering algorithm have been used to put a boundary between sad speech state against the neutral utterances using voice quality features such as jitter, shimmer, noise to harmonic ratio (NHR) and harmonic to noise ratio (HNR). Shimmer has shown highest accuracy among all these features for sad state followed by jitter as the result suggest. However, for neutral utterances the accuracy of HNR features is best among all followed by shimmer.