Modeling Topic Coherence for Speech Recognition
نویسنده
چکیده
Statistical language models play a ma jor role in current speech recognition sys tems Most of these models have fo cussed on relatively local interactions be tween words Recently however there have been several attempts to incorpo rate other knowledge sources in par ticular longer range word dependencies in order to improve speech recognizers We will present one such method which tries to automatically utilize properties of topic continuity When a base line speech recognition system generates al ternative hypotheses for a sentence we will utilize the word preferences based on topic coherence to select the best hy pothesis In our experiment we achieved a improvement in the word er ror rate on top of the base line system It corresponds to of the possible word error improvement
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