Hemanta Kumar Palo

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The challenge to enhance the naturalness and efficiency of spoken language man–machine interface, emotional speech identification and its classification has been a predominant research area. The reliability and accuracy of such emotion identification greatly depends on the feature selection and extraction. In this paper, a combined feature selection(More)
Bio-medical research extends towards human voice and auditory systems day by day. Similarly it helps for the security issues. Emotion analysis and recognition for such purpose is a challenging task. To analyze and recognize, the emotions has been attempted in this piece of work. Initially, Sub-band spectral features have been extracted to characterize high(More)
Clarity and intelligibility in speech signal demands removal of noise and interference associated with the signal at the source. This poses further challenge when the speech signal is colored with human emotions. In this work, the authors have taken a novel step to enhance the emotional speech signal adaptively before classification. Most popular adaptive(More)
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(More)
The occurrence of noise in almost all types of signals is natural. Though the noise variants are many, the impulsive noise in signal highly affects its quality. In this piece of work, speech signal is considered for enhancement that is contaminated with impulsive noise. Generally, hiccups create such type of noise due to tiredness or myoclonic problem of(More)
Emotion recognition from human speech is a challenge for the researchers. It is mostly considered under ideal acoustic conditions. The performance of such system is degraded while there is existence of environmental mismatches between training and testing phases. For robust speech recognition it requires for reduction of redundancy, variability, and(More)
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