Foundations of statistical natural language processing
Foundations of statistical natural language processing
A corpus-based speech synthesis system with emotion
Speech Communication - Special issue on speech and emotion
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 2 - Volume 2
Voice conversion using duration-embedded bi-HMMs for expressive speech synthesis
IEEE Transactions on Audio, Speech, and Language Processing
Prosody conversion from neutral speech to emotional speech
IEEE Transactions on Audio, Speech, and Language Processing
Emotion recognition and conversion for mandarin speech
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1
Voice conversion using partial least squares regression
IEEE Transactions on Audio, Speech, and Language Processing
Emotion conversion based on prosodic unit selection
IEEE Transactions on Audio, Speech, and Language Processing
Hierarchical prosody conversion using regression-based clustering for emotional speech synthesis
IEEE Transactions on Audio, Speech, and Language Processing
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
Hi-index | 14.98 |
In emotional speech synthesis, a large speech database is required for high quality speech output. Voice conversion needs only a compact-sized speech database for each emotion. This study designs and accumulates a set of phonetically balanced small-sized emotional parallel speech databases to construct conversion functions. Gaussian mixture bi-gram model (GMBM) is adopted as the conversion function to characterize the temporal and spectral evolution of the speech signal. The conversion function is initially constructed for each instance of parallel sub-syllable pairs in the collected speech database. To reduce the total number of conversion functions, and select an appropriate conversion function, this study presents a framework by incorporating linguistic and spectral information for conversion function clustering and selection. Subjective and objective evaluations with statistical hypothesis testing are conducted to evaluate the quality of the converted speech. The proposed method compares favorably with previous methods in conversion-based emotional speech synthesis.