Prosody Generation with a Neural Network: Weighing the Importance of Input Parameters
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 2 - Volume 2
Prosodic manipulation using instants of significant excitation
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
Using a sigmoid transformation for improved modeling of phoneme duration
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 01
Springer Handbook of Speech Processing
Springer Handbook of Speech Processing
Bayesian networks for phone duration prediction
Speech Communication
Determinism in speech pitch relation to emotion
Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
Application of prosody models for developing speech systems in Indian languages
International Journal of Speech Technology
International Journal of Speech Technology
Emotion recognition from speech using source, system, and prosodic features
International Journal of Speech Technology
Characterization and recognition of emotions from speech using excitation source information
International Journal of Speech Technology
Computer Speech and Language
A fuzzy classifier to deal with similarity between labels on automatic prosodic labeling
Computer Speech and Language
Identification of Indian languages using multi-level spectral and prosodic features
International Journal of Speech Technology
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In this paper we propose models for predicting the intonation for the sequence of syllables present in the utterance. The term intonation refers to the temporal changes of the fundamental frequency (F"0). Neural networks are used to capture the implicit intonation knowledge in the sequence of syllables of an utterance. We focus on the development of intonation models for predicting the sequence of fundamental frequency values for a given sequence of syllables. Labeled broadcast news data in the languages Hindi, Telugu and Tamil is used to develop neural network models in order to predict the F"0 of syllables in these languages. The input to the neural network consists of a feature vector representing the positional, contextual and phonological constraints. The interaction between duration and intonation constraints can be exploited for improving the accuracy further. From the studies we find that 88% of the F"0 values (pitch) of the syllables could be predicted from the models within 15% of the actual F"0. The performance of the intonation models is evaluated using objective measures such as average prediction error (@m), standard deviation (@s) and correlation coefficient (@c). The prediction accuracy of the intonation models is further evaluated using listening tests. The prediction performance of the proposed intonation models using neural networks is compared with Classification and Regression Tree (CART) models.