Modeling temporal dependency for robust estimation of LP model parameters in speech enhancement
Refereed conference paper presented and published in conference proceedings


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摘要This paper presents a novel approach to robust estimation of linear prediction (LP) model parameters in the application of speech enhancement. The robustness stems from the use of prior knowledge on the clean speech and the interfering noise, which are represented by two separate codebooks of LP model parameters. We propose to model the temporal dependency between short-time model parameters with a composite hidden Markov model (HMM) that is constructed by combining the speech and the noise codebooks. Optimal speech model parameters are estimated from the HMM state sequence that best matches the input observation. To further improve the estimation accuracy, we propose to perform interpolation of multiple HMM state sequences such that the estimated speech parameters would not be limited by the codebook coverage. Experimental results demonstrate the benefits and effectiveness of temporal dependency modeling and states interpolation in improving the segmental signal-to-noise ratio, PESQ and spectral distortion of enhanced speech.
著者Wong C.H., Lee T., Yeung Y.T., Ching P.C.
會議名稱16th Annual Conference of the International Speech Communication Association, INTERSPEECH 2015
會議開始日06.09.2015
會議完結日10.09.2015
會議地點Dresden
會議國家/地區德國
詳細描述International Speech Communication Association
出版年份2015
月份1
日期1
卷號2015-January
頁次1730 - 1734
國際標準書號978-1-5108-1790-6
國際標準期刊號1990-9772
語言英式英語
關鍵詞Hidden Markov model (HMM), Linear predictive (LP) model parameters, Speech enhancement

上次更新時間 2020-12-10 於 00:10