Improved Mandarin Keyword Spotting Using Confusion Garbage Model

This paper presents an improved acoustic keyword spotting (KWS) algorithm using a novel confusion garbage model in Mandarin conversational speech. Observing the KWS corpus, we found there are many words with similar pronunciation with predefined keywords, although they have different Chinese characters and different meanings, which easily result in high false alarm rate. In this paper, an improved acoustic KWS method with confusion garbage models was developed that absorbs similar pronunciation words confused with specific keywords for a given task. One obvious advantage of such method is that it provides a flexible framework to implement the selection procedure and reduce false alarm rate effectively for a specific task. The efficiency of the proposed architecture was evaluated under HMM-based confidence measures (CM) methods and demonstrated on a conversational telephone dataset.