Using prompts to produce quality corpus for training automatic speech recognition systems

International audience— In this paper we present an integrated unsupervised method to produce a quality corpus for training automatic speech recognition system (ASR) using prompts or closed captions. Closed captions and prompts do not always have timestamps and do not necessarily correspond to the exact speech. We propose a method allowing to extract quality corpus from imperfect transcript. The proposed approach works in two steps. During the search, the ASR system finds matching segments in a large prompt database. Matching segments are then used inside a Driven Decoding Algorithm (DDA) to produce a high quality corpus. Results show a F-measure of 96% in term of spotting while the DDA corrects the output according to the prompts: a high quality corpus is easily extracted. 1 Index Terms— speech recognition, closed captioning, corpus building, automatic segmentation I. INTRODUCTION The training of an automatic speech recognition system (ASR) requires large amounts of exact annotated speech. The transcription task is expensive and takes a lot of time. In some situations imperfect transcripts like journalist prompts, closed captions or abstracts are available. This material is available in large quantities. However, these transcripts present two issues: the distance compared to the audio stream and the lack of timestamps. Various approaches propose to use imperfect transcripts for unsupervised ASR training (section II-D). But existing methods are not integrated and have shortcomings: processes are iterative and take a lot of computing time; the lack of times-tamp is forgotten. Moreover existing methods do not use all the potential of imperfect transcripts. The first part of this paper is dedicated to the related work on these issues: the prompts quality, methods to perform ASR alignment with imperfect transcripts, the automatic imperfect transcript segmentation, and finally how to use them for training an ASR system. In a second part, we describe an integrated approach allowing us to solve the two main approximated transcription issues