Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A significant cost in obtaining acoustic training data is the generation of accurate transcriptions. For some sources close-caption data is available. This allows the use of lightly-supervised training techniques. Hover, for some sources and languages close-caption is not available. In these cases unsupervised training techniques must be used.  examines the use of unsupervised techniques for discriminative training. In unsupervised training automatic transcriptions from a recognition system are used for training. As these transcriptions may be errorful data selection may be useful. Two forms of selection are described, one to remove non-target language shows, the other to remove segments with low confidence. Experiments re carried out on a Mandarin transcriptions task. Two types of test data re considered, broadcast news (BN) and broadcast conversations (BC). Results show that the gains from unsupervised discriminative training are highly dependent on the accuracy of the automatic transcriptions.
['Lan Wang', 'Mark J. F. Gales', 'Philip C. Woodland']
I am writing a research paper on a idea Unsupervised Training for Mandarin Broadcast News and Conversation Transcription
[{"from": "human", "value": "I am writing a research paper on a idea Unsupervised Training for Mandarin Broadcast News and Conversation Transcription"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A significant cost in obtaining acoustic training data is the generation of accurate transcriptions. For some sources close-caption data is available. This allows the use of lightly-supervised training techniques. Hover, for some sources and languages close-caption is not available. In these cases unsupervised training techniques must be used.  examines the use of unsupervised techniques for discriminative training. In unsupervised training automatic transcriptions from a recognition system are used for training. As these transcriptions may be errorful data selection may be useful. Two forms of selection are described, one to remove non-target language shows, the other to remove segments with low confidence. Experiments re carried out on a Mandarin transcriptions task. Two types of test data re considered, broadcast news (BN) and broadcast conversations (BC). Results show that the gains from unsupervised discriminative training are highly dependent on the accuracy of the automatic transcriptions."}]