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Kalman tracking linear predictor for vowel intelligibility enhancement on european portuguese HMM based speech synthesis
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Orientador(es)
Resumo(s)
The recent developments on Hidden Markov Models (HMM) based speech synthesis showed that this is a promising technology fully capable of competing with other established techniques. However some issues still lack a solution. Several authors report an over-smoothing phenomenon on both time and frequencies which decreases naturalness and sometimes intelligibility. In this work we present a new vowel
intelligibility enhancement algorithm that uses a discrete Kalman filter (DKF) for tracking frame based parameters.
The inter-frame correlations are modelled by an autoregressive structure which provides an underlying time frame dependency and can improve time-frequency resolution. The system’s performance has been evaluated using objective and subjective tests and the proposed methodology has led to improved results.
Descrição
Palavras-chave
Kalman filtering Speech intelligibility
Contexto Educativo
Citação
Coelho, L., Braga, D., & Garcia-Mateo, C. (2010). Kalman tracking linear predictor for vowel intelligibility enhancement on european portuguese HMM based speech synthesis. 2010 IEEE International Conference on Acoustics, Speech and Signal Processing. Institute of Electrical & Electronics Engineers (IEEE). http://doi.org/10.1109/icassp.2010.5495168