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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
decoding engine. Such measures are useful to drive the recognition process by modifying the likelihood score or to validate recognized words in on-the-fly applications as keyword spotting task and on-line automatic speech transcription for deaf people. Two kinds of results are given. Firstly, an EER evaluation on a French
One of the most important steps in a keyword spotting (KWS) system is a post-processing procedure to compute a confidence measure (CM) for each hypothesized keyword. The CM is commonly estimated by likelihood-based acoustic scores. However durations of the detected keyword, which include useful information, has not
associated with the creation of test sentences for the children hearing loss measurement. Special emphasis is given to determine the ratio S/N at which the intelligibility of keywords is 50%.
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