A Statistical Model-Based Speech Enhancement Using Acoustic Noise Classification for Robust Speech Communication
- Authors
- Choi, Jae-Hun; Chang, Joon-Hyuk
- Issue Date
- Jul-2012
- Publisher
- IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG
- Keywords
- statistical model-based speech enhancement; Gaussian mixture model; noise classification
- Citation
- IEICE TRANSACTIONS ON COMMUNICATIONS, v.E95B, no.7, pp.2513 - 2516
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEICE TRANSACTIONS ON COMMUNICATIONS
- Volume
- E95B
- Number
- 7
- Start Page
- 2513
- End Page
- 2516
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/27510
- DOI
- 10.1587/transcom.E95.B.2513
- ISSN
- 0916-8516
- Abstract
- In this paper, we present a speech enhancement technique based on the ambient noise classification that incorporates the Gaussian mixture model (GMM). The principal parameters of the statistical model-based speech enhancement algorithm such as the weighting parameter in the decision-directed (DD) method and the long-term smoothing parameter of the noise estimation, are set according to the classified context to ensure best performance under each noise. For real-time context awareness, the noise classification is performed on a frame-by-frame basis using the GMM with the soft decision framework. The speech absence probability (SAP) is used in detecting the speech absence periods and updating the likelihood of the GMM.
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