Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  considers discriminative training of language models for large vocabulary continuous speech recognition. The minimum word error (MWE) criterion was explored to make use of the word confusion information as ll as the local lexical constraints inherent in the acoustic training corpus, in conjunction with those constraints obtained from the background text corpus, for properly guiding the speech recognizer to separate the correct hypothesis from the competing ones. The underlying characteristics of the MWEbased approach re extensively investigated, and its performance was verified by comparison with the conventional maximum likelihood (ML) approaches as ll. The speech recognition experiments re performed on the broadcast news collected in Taiwan.
['Jen Wei Kuo', 'Berlin Chen']
I am writing a research paper on a idea MINIMUM WORD ERROR BASED DISCRIMINATIVE TRAINING OF LANGUAGE MODELS
[{"from": "human", "value": "I am writing a research paper on a idea MINIMUM WORD ERROR BASED DISCRIMINATIVE TRAINING OF LANGUAGE MODELS"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  considers discriminative training of language models for large vocabulary continuous speech recognition. The minimum word error (MWE) criterion was explored to make use of the word confusion information as ll as the local lexical constraints inherent in the acoustic training corpus, in conjunction with those constraints obtained from the background text corpus, for properly guiding the speech recognizer to separate the correct hypothesis from the competing ones. The underlying characteristics of the MWEbased approach re extensively investigated, and its performance was verified by comparison with the conventional maximum likelihood (ML) approaches as ll. The speech recognition experiments re performed on the broadcast news collected in Taiwan."}]