Building continuous space language models for transcribing european languages
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TL;DR
The recognition of French Broadcast News and English and Spanish parliament speeches is addressed, tasks for which less resources are available, and a neural network language model is applied that takes better advantage of the limited amount of training data.
Abstract
Large vocabulary continuous speech recognizers for English Broadcast News achieve today word error rates below 10%. An important factor for this succes is the availability of large amounts of acoustic and language modeling training data. In this paper the recognition of French Broadcast News and English and Spanish parliament speeches is addressed, tasks for which less resources are available. A neural network language model is applied that takes better advantage of the limited amount of training data. This approach performs the estimation of the probabilities in a continuous space, allowing by this means smooth interpolations. Word error reduction of up to 0.9% absolute are reported with respect to a carefully tuned backoff language model trained on the same data.