Multi-stream adaptive evidence combination for noise robust ASR
| Autor: | Andrew C. Morris, Hervé Glotin, Hervé Bourlard, Astrid Hagen |
|---|---|
| Rok vydání: | 2001 |
| Předmět: |
Linguistics and Language
Speech perception noise adaptation Computer science speech Communication Speech recognition Posterior probability Speech processing computer.software_genre robust ASR Language and Linguistics Expert system Computer Science Applications evidence combination Computer Science::Sound Robustness (computer science) Modeling and Simulation Computer Vision and Pattern Recognition Psychoacoustics Hidden Markov model Latent variable model multi-stream processing computer Software |
| Zdroj: | Speech Communication. 34:25-40 |
| ISSN: | 0167-6393 |
| DOI: | 10.1016/s0167-6393(00)00044-3 |
| Popis: | In this paper we develop different mathematical models in the framework of the multi-stream paradigm for noise robust ASR, and discuss their close relationship with human speech perception. Largely inspired by Fletcher's "product-of-errors" rule in psychoacoustics, multi-band ASR aims for robustness to data mismatch through the exploitation of spectral redundancy, while making minimum assumptions about noise type. Previous ASR tests have shown that independent sub-band processing can lead to decreased recognition performance with clean speech. We have overcome this problem by considering every combination of data sub-bands as an independent data stream. After introducing the background to multi-band ASR, we show how this "full combination" approach can be formalised, in the context of HMM/ANN based ASR, by introducing a latent variable to specify which data sub-bands in each data frame are free from data mismatch. This enables us to decompose the posterior probability for each phoneme into a reliability weighted integral over all possible positions of clean data. This approach offers great potential for adaptation to rapidly changing and unpredictable noise. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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