5th European Conference on Speech Communication and Technology

Rhodes, Greece
September 22-25, 1997

Explicit Word Error Minimization in N-Best List Rescoring

Andreas Stolcke, Yochai Konig, Mitchel Weintraub

Speech Technology and Research Laboratory SRI International, Menlo Park, CA, USA

We show that the standard hypothesis scoring paradigm used in maximum-likelihood-based speech recognition systems is not optimal with regard to minimizing the word error rate, the commonly used performance metric in speech recognition. This can lead to sub- optimal performance, especially in high-error-rate environments where word error and sentence error are not necessarily monotonically related. To address this discrepancy, we developed a new algorithm that explicitly minimizes expected word error for recognition hypotheses. First, we approximate the posterior hypothesis probabilities using N-best lists. We then compute the expected word error for each hypothesis with respect to the posterior distribution, and choose the hypothesis with the lowest error. Experiments show improved recognition rates on two spontaneous speech corpora.

Full Paper

Bibliographic reference.  Stolcke, Andreas / Konig, Yochai / Weintraub, Mitchel (1997): "Explicit word error minimization in n-best list rescoring", In EUROSPEECH-1997, 163-166.