Innovative approaches for large vocabulary name recognition

Abstract

Automatic name dialing is a practical and interesting application of speech recognition on telephony systems. The IBM name recognition system is a large vocabulary, speaker independent system currently in use for reaching IBM employees in the United States. In this paper, we present some innovative algorithms that improve name recognition accuracy. Unlike transcription tasks, such as the Switchboard task, recognition of names poses a variety of different problems. Several of these problems arise from the fact that foreign names are hard to pronounce for speakers who are not familiar with the names and that there are no standardized methods for pronouncing proper names. Noise robustness is another very important factor as these calls are typically made in noisy environments, such as from a car, cafeteria, airport, etc. and over different kinds of cellular and land-line telephone channels. We have performed a systematic analysis of the speech recognition errors and tackled the issues separately with techniques ranging from weighted speaker clustering, massive adaptation, rapid and unsupervised adaptation methods to pronounciation modeling methods. We find that the decoding accuracy can be improved significantly (28% relative) in this manner.