Transcription of broadcast news
Jean‐Luc Gauvain, Lori F Lamel, Gilles Adda, Martine Adda‐Decker · 1997
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...