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Journal Article
Statistical Model-Based Noise Reduction Approach for Car Interior Applications to Speech Recognition
- Authors
- Sung Joo Lee, Byung Ok Kang, Ho-Young Jung, Yunkeun Lee, Hyung Soon Kim
- Issue Date
- 2010-10
- Citation
- ETRI Journal, v.32, no.5, pp.801-809
- ISSN
- 1225-6463
- Publisher
- 한국전자통신연구원 (ETRI)
- Language
- English
- Type
- Journal Article
- Abstract
- This paper presents a statistical model-based noise suppression approach for voice recognition in a car environment. In order to alleviate the spectral whitening and signal distortion problem in the traditional decisiondirected Wiener filter, we combine a decision-directed method with an original spectrum reconstruction method and develop a new two-stage noise reduction filter estimation scheme. When a tradeoff between the performance and computational efficiency under resource-constrained automotive devices is considered, ETSI standard advance distributed speech recognition font-end (ETSI-AFE) can be an effective solution, and ETSI-AFE is also based on the decision-directed Wiener filter. Thus, a series of voice recognition and computational complexity tests are conducted by comparing the proposed approach with ETSI-AFE. The experimental results show that the proposed approach is superior to the conventional method in terms of speech recognition accuracy, while the computational cost and frame latency are significantly reduced. © 2010 ETRI.
- KSP Keywords
- Computational Efficiency, Computational complexity, Conventional methods, Distributed speech recognition(DSR), Noise Reduction(NR), Noise Suppression, Reconstruction method, Resource-constrained, Speech recognition accuracy, Statistical Model, Two-Stage