Speech recognition on Mandarin call home: a large-vocabulary, conversational, and telephone speech corpus

Abstract

In this paper we describe IBM's most recent efforts for speech recognition on a conversational-speech database, the Mandarin Call Home corpus. While it is similar to the well-known Switchboard corpus, the Call Home task addresses several major challenges in the domain of spoken language systems, including spontaneous dialogue with no pre-specified topics, limited-bandwidth telephone signal, and recognition of other languages than English. In this paper, we particularly describe the methodology used in Mandarin Call Home corpus to address language-specific issues. We also examine and compare our results with those of the English Switchboard corpus. Preliminary experiments show that a 58.7% character error rate can be achieved in the context of April 95 Mandarin Call Home data set. The experimental results are comparable to those of the state-of-the-art IBM Switchboard system with similar amount of training data.