Combining Phoneme Loop Posteriori with Decoding Posteriori as Confidence Measure for Speech Recognition in E-service

Abstract

This paper presents our confidence measure system for speech recognition to integrate with e-Service to make HumanComputer Interaction more convenient. In order to make the system more robust for practical usage, the confidence measure is optimized to improve its performance as well as speed, compared with traditional state based confidence measure. First, the decoding likelihood of the best path is normalized with all the survival paths to form the onepass-based posteriori. After decoding, when recognition result is available as well as the phoneme level division point, the phoneme loop posteriori based confidence is calculated. Different models are compared for speed and performance. Then they are combined to form the final confidence for the judgement. Experiments are designed, and the proposed confidence measure get a relative improvement of 20%, 19.33% for equal error rate and 37.19%, 35.17% of false acceptance rate for out-of-vocabulary set on the development sets and the test sets, with no loss of false rejection rate for in-vocabulary set.

Enhanced Out of Vocabulary Word Detection Using Local Acoustic Information

“…The second-pass search will rescore these hypotheses which Figure 1. Baseline system for spoken term detection will be returned with phoneme loop posterior probabilities (PLPP) [11]. The two scores calculated by the two pass search will be combined as a linear form for re-ranking as shown below:…”