Context-dependent factored language models

Abstract

The incorporation of grammatical information into speech recognition systems is often used to increase performance in morphologically rich languages. However, this introduces demands for sufficiently large training corpora and proper methods of using the additional information. In this paper, we present a method for building factored language models that use data obtained by morphosyntactic tagging. The models use only relevant factors that help to increase performance and ignore data from other factors, thus also reducing the need for large morphosyntactically tagged training corpora. Which data is relevant is determined at run-time, based on the current text segment being estimated, i.e., the context. We show that using a context-dependent model in a two-pass recognition algorithm, the overall speech recognition accuracy in a Broadcast News application improved by 1.73% relatively, while simpler models using the same data achieved only 0.07% improvement. We also present a more detailed error analysis based on lexical features, comparing first-pass and second-pass results.

Machine Translation and the Evaluation of Its Quality

“…One group of morphologically rich languages is a group of highly inflected languages. They are difficult not only for MT but also for other language technology applications [4,5]. The main problem in highly inflected languages is that the large number of inflected word forms lead to data sparsity (see example in Table 1), which results in unreliable estimates in statistical MT [6].…”

Development of Voice and Face Recognition Based Security Software for Biometric Systems

“…In the United States, the National Security Agency (National Security Agency) uses a speech recognition type for keyword detection [37,38]. A study was performed to improve the quality and accuracy of the speech recognition system using a multimodal audiovisual speech signal [39,40]. They made a study called factored language models.…”