Confidence Measure Based on Context Consistency Using Word Occurrence Probability and Topic Adaptation for Spoken Term Detection

Abstract

SUMMARYIn this paper, we propose a novel confidence measure to improve the performance of spoken term detection (STD). The proposed confidence measure is based on the context consistency between a hypothesized word and its context in a word lattice. The main contribution of this paper is to compute the context consistency by considering the uncertainty in the results of speech recognition and the effect of topic. To measure the uncertainty of the context, we employ the word occurrence probability, which is obtained through combining the overlapping hypotheses in a word posterior lattice. To handle the effect of topic, we propose a method of topic adaptation. The adaptation method firstly classifies the spoken document according to the topics and then computes the context consistency of the hypothesized word with the topic-specific measure of semantic similarity. Additionally, we apply the topic-specific measure of semantic similarity by two means, and they are performed respectively with the information of the top-1 topic and the mixture of all topics according to topic classification. The experiments conducted on the Hub-4NE Mandarin database show that both the occurrence probability of context word and the topic adaptation are effective for the confidence measure of STD. The proposed confidence measure performs better compared with the one ignoring the uncertainty of the context or the one using a non-topic method.

Spoken term detection ALBAYZIN 2014 evaluation: overview, systems, results, and discussion

“…There are two main approaches to STD: the wordbased approach [6,[41][42][43][44][45] that searches for terms in the output of a large vocabulary continuous speech recognition (LVCSR) system, and the subword-based approach which searches for subword representations of search terms within the output of a subword speech recognition system. The word-based STD approach typically obtains better performance than the subword-based approach thanks to the lexical information it employs.…”