Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Structured language models have recently been shown to give significant improvements in large-vocabulary recognition relative to traditional word N-gram models, but typically imply a heavy computational burden and have not been applied to large training sets or complex recognition systems. Previously,  developed a linguistically motivated and computationally efficient almost-parsing language model, using a data structure derived from constraint dependency grammar parsing, that tightly integrates knowledge of words, lexical features, and syntactic constraints.  show that such a model can be used effectively and efficiently in all stages of a complex, multi-pass conversational telephone speech recognition system. Compared to a state-of-the-art 4-gram interpolated word- and class-based language model,  obtained a 6.2% relative word error reduction (a 1.6% absolute reduction) on a recent NIST evaluation set.
['Wen Wang', 'Andreas Stolcke', 'Mary P. Harper']
I am writing a research paper on a idea The use of a linguistically motivated language model in conversational speech recognition
[{"from": "human", "value": "I am writing a research paper on a idea The use of a linguistically motivated language model in conversational speech recognition"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Structured language models have recently been shown to give significant improvements in large-vocabulary recognition relative to traditional word N-gram models, but typically imply a heavy computational burden and have not been applied to large training sets or complex recognition systems. Previously,  developed a linguistically motivated and computationally efficient almost-parsing language model, using a data structure derived from constraint dependency grammar parsing, that tightly integrates knowledge of words, lexical features, and syntactic constraints.  show that such a model can be used effectively and efficiently in all stages of a complex, multi-pass conversational telephone speech recognition system. Compared to a state-of-the-art 4-gram interpolated word- and class-based language model,  obtained a 6.2% relative word error reduction (a 1.6% absolute reduction) on a recent NIST evaluation set."}]