Máté Szarvas

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This article introduces a novel approach to model morphosyntax in morpheme unit based speech recognizers. The proposed method is evaluated in our recent Hungarian large vocabulary continuous speech recognition (LVCSR) system. The architecture of the recognition system is based on the weighted finite state transducer (WFST) paradigm. The task domain is the(More)
This article describes the design and the experimental evaluation of the first Hungarian large vocabulary continuous speech recognition (LVCSR) system. The architecture of the recognition system is based on the recently proposed weighted finite state transducer (WFST) paradigm. The task domain is the recognition of fluently read sentences selected from a(More)
In this article we evaluate our stochastic morphosyntactic language model (SMLM) on a Hungarian newspaper dictation task that requires modeling over 1 million different word forms. The proposed method is based on the use of morphemes as the basic recognition units and the combination of a morpheme AE-gram model and a morphosyntactic language model. The(More)
This paper describes a novel method that models the correlation between acoustic observations in contiguous speech segments. The basic idea behind the method is that acoustic observations are conditioned not only on the phonetic context but also on the preceding acoustic segment observation. The correlation between consecutive acoustic observations is(More)
This article introduces a novel approach to model phonology and morphosyntax in morpheme unit based speech recognizers. The proposed method is evaluated in our recent Hungarian large vocabulary continuous speech recognition (LVCSR) system. The architecture of the recognition system is based on the weighted finite state transducer (WFST) paradigm. The task(More)