Transcription of multi-genre media archives using out-of-domain data

We describe our work on developing a speech recognition system for multi-genre media archives. The high diversity of the data makes this a challenging recognition task, which may benefit from systems trained on a combination of in-domain and out-of-domain data. Working with tandem HMMs, we present Multi-level Adaptive Networks (MLAN), a novel technique for incorporating information from out-of-domain posterior features using deep neural networks. We show that it provides a substantial reduction in WER over other systems, with relative WER reductions of 15 % over a PLP baseline, 9 % over in-domain tandem features and 8 % over the best out-of-domain tandem features

Comparison Of Language Modelling Techniques For Russian And English

In this paper the main differences between language modelling of Russian and English are examined. A Russian corpus and a comparable English corpus are described. The effects of high inflectionality in Russian and the relationship between the outof -vocabulary rate and vocabulary size are investigated. Standard word and class N-gram language modelling techniques are applied to the two corpora and perplexity results are reported. A novel approach to the modelling of inflected languages is proposed and its efficacy compared with the other techniques. 1. INTRODUCTION Much work has been conducted in recent years on language modelling techniques for speech recognition of English. In contrast, less commercially attractive yet widely spoken languages like Russian have received comparatively little attention in the literature (the first reported large-vocabulary recogniser for Russian appeared only recently[3]). Moreover, there are important difficulties with modelling Russian which are also..