A New ASR Evaluation Measure and Minimum Bayes-Risk Decoding for Open-domain Speech Understanding
Hajime Nanjo, R. University, Tatsuya Kawahara · 2006
A new evaluation measure of speech recognition and a decoding strategy for keyword-based open-domain speech understanding are presented. Conventionally, WER (word error rate) has been widely used as an evaluation measure of speech recognition, which treats all words in a uniform manner. We define a weighted keyword error rate (WKER) which gives a weight on errors from a viewpoint of information retrieval. We first demonstrate that this measure is more appropriate for predicting the performance of key sentence indexing of oral presentations. Then, we formulate a decoding method to minimize WKER based on a minimum Bayes-risk (MBR) framework, and show that the decoding method works reasonably for improving WKER and key sentence indexing.