Zhiwei Shuang

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.

Comparison of Syllable/Phone HMM Based Mandarin TTS

The performance of HMM-based text to speech (TTS) system is affected by the basic modeling units and the size of training data. This paper compares two HMM based Mandarin TTS systems using syllable and phone as basic units respectively with 1000, 3000 and 5000 sentences’ training data. Two female speakers’ corpora are used as training data for evaluation. For both corpora, the system using syllable as basic unit outperforms the system using phone as basic unit with 3000 and 5000 sentences’ training data.

Automatic Pronunciation Transliteration for Chinese-English Mixed Language Keyword Spotting

This paper presents automatic pronunciation transliteration method with acoustic and contextual analysis for Chinese-English mixed language keyword spotting (KWS) system. More often, we need to develop robust Chinese-English mixed language spoken language technology without Chinese accented English acoustic data. In this paper, we exploit pronunciation conversion method based on syllable-based characteristic analysis of pronunciation and data-driven phoneme pairs mappings to solve mixed language problem by only using well-trained Chinese models. One obvious advantage of such method is that it provides a flexible framework to implement the pronunciation conversion of English keywords to Chinese automatically. The efficiency of the proposed method was demonstrated under KWS task on mixed language database.