Abstract
Vector Taylor series (VTS)-based noise suppression usually employs a single Gaussian distribution for the noise model. However, it is insufficient for non-stationary noise which has a multi-peak distribution. It is very complex to estimate multi-peak distribution of the noise, when we deal with the noise as random variables or hidden variables. To solve these problems, we investigate a way of estimating the noise mixture model by using a minimum mean squared error (MMSE) estimate of the noise. By iterating the MMSE estimation of noise and noise model estimation, the proposed method realizes the simultaneous optimization of both the observed signal model and the noise model. The proposed method significantly outperformed the VTS-based approach, and the maximum improvement in the word error rate was about 12%.
| Original language | English |
|---|---|
| Pages (from-to) | 697-700 |
| Number of pages | 4 |
| Journal | Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH |
| Publication status | Published - 2011 |
| Externally published | Yes |
| Event | 12th Annual Conference of the International Speech Communication Association, INTERSPEECH 2011 - Florence, Italy Duration: 2011 Aug 27 → 2011 Aug 31 |
Keywords
- MMSE estimation
- Noise model estimation
- Noise suppression
ASJC Scopus subject areas
- Language and Linguistics
- Human-Computer Interaction
- Signal Processing
- Software
- Modelling and Simulation