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EnglishIn this paper, we present an acoustic keyword spotter that operates in two stages, detection and verification. In the detection stage, keywords are detected in the utterances, and in the verification stage, confidence measures are used to verify the detected keywords and reject false alarms. A new confidence measure, based on phoneme models trained on an Artificial Neural Network, is used in the verification stage to reduce false alarms. We have found that this ANN-based confidence, together with existing HMM-based confidence measures, is very effective in rejecting false alarms. Experiments are performed on two Mandarin databases and our results show that the proposed method is able to significantly reduce the number of false alarms
| Titolo: | A new confidence measure combining Hidden Markov Models and Artificial Neural Networks of phonemes for effective keyword spotting |
| Autori: | |
| Data di pubblicazione: | 2012 |
| Abstract: | In this paper, we present an acoustic keyword spotter that operates in two stages, detection and verification. In the detection stage, keywords are detected in the utterances, and in the verification stage, confidence measures are used to verify the detected keywords and reject false alarms. A new confidence measure, based on phoneme models trained on an Artificial Neural Network, is used in the verification stage to reduce false alarms. We have found that this ANN-based confidence, together with existing HMM-based confidence measures, is very effective in rejecting false alarms. Experiments are performed on two Mandarin databases and our results show that the proposed method is able to significantly reduce the number of false alarms |
| Handle: | http://hdl.handle.net/11387/77329 |
| ISBN: | 9781467325059 9781467325066 9781467325073 |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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http://hdl.handle.net/11387/77329
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