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This paper presents our recent attempt to make a super-large scale spoken-term detection system, which can detect any keyword uttered in a 2,000-hour speech database within a few seconds. There are three problems to achieve such a system. The system must be able to detect out-of-vocabulary (OOV) terms (OOV problem
The authors present some experiments that show the capabilities of using recurrent neural networks (RNNs) in conjunction with hidden Markov models (HMMs) in the context of keyword spotting (KWS): the automatic recognition of a small set of keywords as they occur in unconstrained speech and/or noise. KWS is usually
Keyword spotting becomes a very important branch of speech recognition. But the acoustic mismatch between training and testing environments often causes a severe degradation in the recognition performance. This paper presents an improved keyword spotting strategy. A fuzzy search algorithm is proposed to extract
accurately locate the occurrences of a list of keywords in a broadcast corpus. Textual information from the transcripts and an efficient rescoring scheme are used to improve the performance of the phonetic search. Our experiments show that the proposed method outperforms the baseline textual and phonetic searches by its ability
. Experiments carried out on conversational corpora for the keyword spotting task in the Chinese 2005 863 Evaluation show that this method can not only yield highly compact SCN lattices with syllable graph density (SGD) of 3.83, but also achieve an equal error rate (EER) of 32.45%, which is about 33% relatively reduction when
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