Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Large vocabulary speech recognition systems fail to recognize words beyond their vocabulary, many of which are information rich terms, like named entities or foreign words. Hybrid word/sub-word systems solve this problem by adding sub-word units to large vocabulary word based systems; new words can then be represented by combinations of sub-word units. Previous work heuristically created the sub-word lexicon from phonetic representations of text using simple statistics to select common phone sequences.  propose a probabilistic model to learn the subword lexicon optimized for a given task.  consider the task of out of vocabulary (OOV) word detection, which relies on output from a hybrid model. A hybrid model with our learned sub-word lexicon reduces error by 6.3% and 7.6% (absolute) at a 5% false alarm rate on an English Broadcast News and MIT Lectures task respectively.
['Carolina Parada', 'Mark Dredze', 'Abhinav Sethy', 'Ariya Rastrow']
I am writing a research paper on a idea Learning Sub-Word Units for Open Vocabulary Speech Recognition
[{"from": "human", "value": "I am writing a research paper on a idea Learning Sub-Word Units for Open Vocabulary 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: Large vocabulary speech recognition systems fail to recognize words beyond their vocabulary, many of which are information rich terms, like named entities or foreign words. Hybrid word/sub-word systems solve this problem by adding sub-word units to large vocabulary word based systems; new words can then be represented by combinations of sub-word units. Previous work heuristically created the sub-word lexicon from phonetic representations of text using simple statistics to select common phone sequences.  propose a probabilistic model to learn the subword lexicon optimized for a given task.  consider the task of out of vocabulary (OOV) word detection, which relies on output from a hybrid model. A hybrid model with our learned sub-word lexicon reduces error by 6.3% and 7.6% (absolute) at a 5% false alarm rate on an English Broadcast News and MIT Lectures task respectively."}]