Extending boosting for call classification using word confusion networks
Gökhan Tür, Dilek Zeynep Hakkani-Tür, Giuseppe Riccardi · 2004
We are interested in the problem of robust understanding from noisy spontaneous speech input. In goal driven human-machine dialog, utterance classification is a key component of the understanding process to determine the intent of the speaker. We propose a novel algorithm for exploiting ASR word confidence scores for better classification of spoken utterances. Word confidence scores for automatic speech recognition (ASR) provide estimates for word error rates. While previous work has focused on straightforward combination of word confidence scores into Bayesian classifiers, we extend the mathematical formulation for boosting classifiers. This extension of the algorithm allows confidence scores to be exploited from a 1-best ASR output or from word confusion networks (WCNs). We present methods for on-line and off-line score combinations. The results we show are for a large database of utterances collected using the AT&T VoiceTone/sup SM/ spoken dialog system. Our experiments show between 5% and 10% reduction in error (1-precision) for a given recall using WCNs compared to ASR output.