Integration of n-best recognition results obtained by multiple noise reduction algorithms
Takeshi Yamada, Jiro Okada, Nobuhiko Kitawaki · 2004
During the last decade, a number of noise reduction algo-rithms were proposed for realizing noise robust speech recognition. However, their effectiveness strongly de-pends on noise conditions. One way for solving this problem is to select an optimal algorithm every time be-fore or after recognition process. This paper proposes a new method for integrating N-best recognition results ob-tained by multiple noise reduction algorithms. The pro-posed method selects the best recognition result by us-ing a confidence measure based on a frame-normalized log likelihood score. To evaluate the performance of the proposed method, recognition experiments were per-formed on the AURORA-2J connected digit recognition task. These results confirmed that the proposed method is very effective in the high and middle SNR conditions. 1.