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 ARPA Nov96, Nov97 and Nov98 evaluations. 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).

In this paper we report on our recent work in transcribing broadcast news shows. Radio and television broadcasts contain signal segments of various linguistic and acoustic natures. The shows contain both prepared and spontaneous speech. The signal may be studio quality or have been transmitted over a telephone or other noisy channel (ie., corrupted by additive noise and nonlinear distorsions), or may contain speech over music. Transcription of this type of data poses challenges in dealing with the continuous stream of data under varying conditions. Our approach to this problem is to segment the data into a set of categories, which are then processed with category specific acoustic models. We describe our 65k speech recognizer and experiments using different sets of acoustic models for transcription of broadcast news data. The use of prior knowledge of the segment boundaries and types is shown to not crucially affect the performance. 1. INTRODUCTION The goal of this research is to au...

While significant improvements have been made over the last 5 years in large vocabulary continuous speech recognition of large read-speech corpora such as the ARPA Wall Street Journal-based CSR corpus (WSJ) for American English and the BREF corpus for French, these tasks remain relatively artificial. In this paper we report on our development work in moving from laboratory read speech data to real-world speech data in order to build a system for the new ARPA broadcast news transcription task. The LIMSI Nov96 speech recognizer makes use of continuous density HMMs with Gaussian mixture for acoustic modeling and n- gram statistics estimated on newspaper texts. The acoustic models are trained on the WSJ0/WSJ1, and adapted using MAP estimation with task-specific training data. The overall word error on the Nov96 partitioned evaluation test was 27.1%. INTRODUCTION Over the last 5 years significant advances have been made in large vocabulary, continuous speech recognition, which has been a...