The LIMSI Broadcast News transcription system

Abstract

This paper reports on activites at LIMSI over the last few years directed at the transcription of broadcast news data. We describe our development work in moving from laboratory read speech data to real-world or `found' speech data in preparation for the DARPA evaluations on this task from 1996 to 1999. Two main problems needed to be addressed to deal with the continuous flow of inhomogenous data. These concern the varied acoustic nature of the signal (signal quality, environmental and transmission noise, music) and different linguistic styles (prepared and spontaneous speech on a wide range of topics, spoken by a large variety of speakers). The problem of partitioning the continuous stream of data is addressed using an iterative segmentation and clustering algorithm with Gaussian mixtures. The speech recognizer makes use of continuous density HMMs with Gaussian mixture for acoustic modeling and 4-gram statistics estimated on large text corpora. Word recognition is performed in multiple passes, where current hypotheses are used for cluster-based acoustic model adaptation prior to the next decoding pass. The overall word transcription error of the LIMSI evaluation systems were 27.1% (Nov96, partitioned test data), 18.3% (Nov97, unpartitioned data), 13.6% (Nov98, unpartitioned data) and 17.1% (Fall99, unpartitioned data with computation time under 10× real-time).