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Adaptive filtering for high quality HMM based speech synthesis
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Orientador(es)
Resumo(s)
In this work an adaptive filtering scheme based on a dual Discrete Kalman Filtering (DKF) is proposed for Hidden Markov Model (HMM) based speech synthesis quality enhancement. The objective is to improve signal smoothness across HMMs and their related states and to reduce artifacts due to acoustic model's limitations. Both speech and artifacts are modelled by an autoregressive structure which provides an underlying time frame dependency and improves time-frequency resolution. Themodel parameters are arranged to obtain a combined state-space model and are also used to calculate instantaneous power spectral density estimates. The quality enhancement is performed by a dual discrete Kalman filter that simultaneously gives estimates for the models and the signals. The system's performance has been evaluated using mean opinion score tests and the proposed technique has led to improved results.
Descrição
Palavras-chave
Kalman filtering Spectral Analysis
Contexto Educativo
Citação
Coelho, L., & Braga, D. (2008). Adaptive filtering for high quality hmm based speech synthesis. 2008 IEEE Spoken Language Technology Workshop. Institute of Electrical & Electronics Engineers (IEEE). http://doi.org/10.1109/slt.2008.4777835
Editora
IEEE