Efficient data selection for spoken document retrieval based on prior confidence estimation using speech and context independent models

This paper proposes an efficient speech sample selection technique that can identify those samples that will be well recognized. Conventional confidence measures can identify well-recognized speech samples, but they require speech recognition to estimate confidence scores. Speech samples with low confidence should not undergo recognition since they yield speech documents that will eventually be rejected. The proposed technique can select the samples that will justify the application of speech recognition. It is based on rapid prior confidence estimation by using speech and context independent models to calculate acoustic likelihood values on a frame-by-frame basis. Tests show that the proposed confidence estimation technique is over 50 times faster than the conventional posterior confidence measure while maintaining equivalent data selection performance for speech recognition and spoken document retrieval.