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Adaptive modeling and high quality spectral estimation for speech enhancement
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Resumo(s)
In this work an adaptive modeling and spectral estimation scheme based on a dual Discrete Kalman Filtering (DKF) is proposed for speech enhancement. Both speech and noise signals are modeled by an autoregressive structure which provides an underlying time frame dependency and improves time-frequency resolution. The model parameters are arranged to obtain a combined state-space model and are also used to calculate instantaneous power spectral density estimates. The speech enhancement is performed by a dual discrete Kalman filter that simultaneously gives estimates for the models and the signals. This approach is particularly useful as a pre-processing module for parametric based speech recognition systems that rely on spectral time dependent models. The system performance has been evaluated by a set of human listeners and by spectral distances. In both cases the use of this pre-processing module has led to improved results.
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Contexto Educativo
Citação
Coelho, L., & Braga, D. (2008). Adaptive Modeling and High Quality Spectral Estimation for Speech Enhancement. In A. Teixeira, V. L. S. de Lima, L. C. de Oliveira, & P. Quaresma (Eds.), Computational Processing of the Portuguese Language (pp. 244–247). Springer Berlin Heidelberg. Retrieved from http://link.springer.com/chapter/10.1007/978-3-540-85980-2_30
Editora
Springer Berlin Heidelberg