Spectral Flatness Analysis for Emotional Speech Synthesis and Transformation
نویسندگان
چکیده
According to psychological research of emotional speech different emotions are accompanied by different spectral noise. We control its amount by spectral flatness according to which the high frequency noise is mixed in voiced frames during cepstral speech synthesis. Our experiments are aimed at statistical analysis of spectral flatness in three emotions (joy, sadness, anger), and a neutral state for comparison. Calculated histograms of spectral flatness distribution are visually compared and modelled by Gamma probability distribution. Obtained statistical parameters and emotional-to-neutral ratios of their mean values show good correlation for both male and female voices and all three emotions.
منابع مشابه
Residual-based speech modification algorithms for text-to-speech synthesis
This paper presents a set of novel algorithms for the signal modification component of concatenative text-to-speech systems. The algorithms described here are based around the LPC analysis/synthesis framework, and achieve prosodic modification by time-domain processing of the LPC residual. The modified residual is then recombined with the all-pole spectral estimate to synthesise the new speech ...
متن کاملClassification of emotional speech using spectral pattern features
Speech Emotion Recognition (SER) is a new and challenging research area with a wide range of applications in man-machine interactions. The aim of a SER system is to recognize human emotion by analyzing the acoustics of speech sound. In this study, we propose Spectral Pattern features (SPs) and Harmonic Energy features (HEs) for emotion recognition. These features extracted from the spectrogram ...
متن کاملOn the limitations of voice conversion techniques in emotion identification tasks
The growing interest in emotional speech synthesis urges effective emotion conversion techniques to be explored. This paper estimates the relevance of three speech components (spectral envelope, residual excitation and prosody) for synthesizing identifiable emotional speech, in order to be able to customize voice conversion techniques to the specific characteristics of each emotion. The analysi...
متن کاملAffective encoding in the speech signal and in event-related brain potentials
A number of perceptual features have been utilized for the characterization of the emotional state of a speaker. However, for automatic recognition suitable objective features are needed. We have examined several features of the speech signal in relation to accentuation and traces of event-related brain potentials (ERPs) during affective speech perception. Concerning the features of the speech ...
متن کاملA Database for Automatic Persian Speech Emotion Recognition: Collection, Processing and Evaluation
Abstract Recent developments in robotics automation have motivated researchers to improve the efficiency of interactive systems by making a natural man-machine interaction. Since speech is the most popular method of communication, recognizing human emotions from speech signal becomes a challenging research topic known as Speech Emotion Recognition (SER). In this study, we propose a Persian em...
متن کامل