Vocal Emotion Conversion Using WSOLA and Linear Prediction

摘要

The paper deals with speech emotion conversion using Waveform Similarity Overlap Add (WSOLA) and subsequent linear prediction analysis for spectral transformation. Duration modification is done by taking the ratio between segment durations of neutral and target speech. After performing modification using WSOLA, the duration modified source speech is time aligned with target and further subjected to linear prediction analysis to yield the LP coefficients. The target emotion is re-synthesised by using the prosody manipulated residual and LPCs from source. The waveform similarity property of WSOLA is exploited to give output with minimal distortion. The proposed algorithm is subjectively and objectively evaluated along with popular TD-PSOLA algorithm. The correlation between synthesised and real target shows an average improvement of 60% across all emotions with the proposed technique.