Getting more from automatic transcripts for semi-supervised language modeling. (March 2016)

• Record Type: Journal Article
• Authors: Novotney, Scott; Schwartz, Richard; Khudanpur, Sanjeev
• Is Part Of: Computer speech & language. Volume 36(2016)
• Page Start: 93
• Page End: 109
• Publication Date: 2016-03
• Subjects: Language modeling -- Automatic speech recognition -- LVCSR -- Low-resource; Speech processing systems -- Periodicals; Automatic speech recognition -- Periodicals; Computers -- Periodicals; Linguistics -- Periodicals; Speech-Language Pathology -- Periodicals; Traitement automatique de la parole -- Périodiques; Reconnaissance automatique de la parole -- Périodiques; Automatic speech recognition; Speech processing systems; Electronic journals; Periodicals; 006.454
• DOI: 10.1016/j.csl.2015.08.007
• Languages: English
• ISSNs: 0885-2308

Abstract

Abstract : Highlights: We analyze why semi-supervised backoff language modeling performs poorly. We motivate MAP adaptation of a log-linear language model. We use automatic transcripts as a prior for language model estimation. We show consistent reduction in WER across a range of low-resource conditions. Abstract: Many under-resourced languages such as Arabic diglossia or Hindi sub-dialects do not have sufficient in-domain text to build strong language models for use with automatic speech recognition (ASR). Semi-supervised language modeling uses a speech-to-text system to produce automatic transcripts from a large amount of in-domain audio typically to augment a small amount of manual transcripts. In contrast to the success of semi-supervised acoustic modeling, conventional language modeling techniques have provided only modest gains. This paper first explains the limitations of back-off language models due to their dependence on long-span n -grams, which are difficult to accurately estimate from automatic transcripts. From this analysis, we motivate a more robust use of the automatic counts as a prior over the estimated parameters of a log-linear language model. We demonstrate consistent gains for semi-supervised language models across a range of low-resource conditions.