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EnglishA scenario concerning hands-free connected digit recognition in a noisy office environment is investigated. An array of six omnidirectional microphones and a corresponding time delay compensation module are used to provide a bamformed signal as input to a Hidden Markov Model (HMM) based recognizer. Two different techniques of phone HMM adaptation have been considered, to reduce the mismatch between training and test conditions. Adaptation material and test aterial were collected in two different sessions. Results show that a digit accuracy close to 98% can be achieved when the talker is at 1.5 m distance from the array. This result has to be compared with 99.5% accuracy obtained by using a close-talk microphone
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| Titolo: | HMM Adaptation for Hands-Free Connected Digit Recognition | |
| Autori: | ||
| Data di pubblicazione: | 1998 | |
| Abstract: | A scenario concerning hands-free connected digit recognition in a noisy office environment is investigated. An array of six omnidirectional microphones and a corresponding time delay compensation module are used to provide a bamformed signal as input to a Hidden Markov Model (HMM) based recognizer. Two different techniques of phone HMM adaptation have been considered, to reduce the mismatch between training and test conditions. Adaptation material and test aterial were collected in two different sessions. Results show that a digit accuracy close to 98% can be achieved when the talker is at 1.5 m distance from the array. This result has to be compared with 99.5% accuracy obtained by using a close-talk microphone | |
| Handle: | http://hdl.handle.net/11582/1464 | |
| Appare nelle tipologie: | 5.12 Altro |
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