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In this paper we report our approaches to accomplishing the very limited resource keyword search (KWS) task in the NIST Open Keyword Search 2015 (OpenKWS15) Evaluation. We devised the methods, first, to attain better acoustic modeling, multilingual and semi-supervised acoustic model training as well as the examplar-based acoustic model training; second, to address the overwhelming out-of-vocabulary...
In this paper, we propose the use of distance and co-occurrence information of word-pairs to improve language modeling. We have empirically shown that, for history-context sizes of up to ten words, the extracted information about distance and co-occurrence complements the -gram language model well, for which learning long-history contexts is inherently difficult. Evaluated on the Wall Street Journal...
This paper reports our study in exploiting the distance and co-occurrence information of word-pairs to improve the n-gram language model. We used these two types of information for modeling the distant context, up to history length of ten. Also we show that the proposed model provides complementary information about the n-gram's context that is unable to be captured by the n-gram model due to data...
This paper presents our effort in collecting a Malay broadcast news (BN) speech corpus to support our research in Malay LVCSR. The 53 hours corpus is recorded from the TV channels in both Singapore and Malaysia over a 9-month period. To facilitate various researches in LVCSR, besides of orthographic transcription, the corpus provides other metadata such as speaking environment type, speaker identity...
We report the development of a Malay conversational speech corpus as part of our research in spontaneous conversational speech LVCSR. This corpus development effort is the collaboration between NTU and USM. The goal is to collect, transcribe, and annotate 50 hours of conversational Malay speech. The conversation is recorded from both close-talk and telephone channels, and both speakers' utterances...
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