Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  addresses error type classification in continuous speech recognition (CSR). In CSR, errors are classified into three types, namely, the substitution, insertion and deletion errors, by making an alignment beten a recognized word sequence and its reference transcription with a dynamic programming (DP) procedure.  propose a method for deriving such alignment features from a word confusion network (WCN) without using the reference transcription.  show experimentally that the WCN-based alignment features steadily improve the performance of error type classification. They also improve the performance of out-of-vocabulary (OOV) word detection, since OOV word utterances are highly correlated with a particular alignment pattern. In addition,  show that the word accuracy can be estimated from the WCN-based alignment features and more accurately from the error type classification result without using the reference transcription.
['Atsunori Ogawa', 'Takaaki Hori', 'Atsushi Nakamura']
I am writing a research paper on a idea Error type classification and word accuracy estimation using alignment features from word confusion network
[{"from": "human", "value": "I am writing a research paper on a idea Error type classification and word accuracy estimation using alignment features from word confusion network"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  addresses error type classification in continuous speech recognition (CSR). In CSR, errors are classified into three types, namely, the substitution, insertion and deletion errors, by making an alignment beten a recognized word sequence and its reference transcription with a dynamic programming (DP) procedure.  propose a method for deriving such alignment features from a word confusion network (WCN) without using the reference transcription.  show experimentally that the WCN-based alignment features steadily improve the performance of error type classification. They also improve the performance of out-of-vocabulary (OOV) word detection, since OOV word utterances are highly correlated with a particular alignment pattern. In addition,  show that the word accuracy can be estimated from the WCN-based alignment features and more accurately from the error type classification result without using the reference transcription."}]