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Journals and Conferences
Despite their known weaknesses, hidden Markov models (HMMs) have been the dominant technique for acoustic modeling in speech recognition for over two decades. Still, the advances in the HMM framework… (More)
Nowadays read speech recognition already works pretty well, but the recognition of spontaneous speech is much more problematic. There are plenty of reasons for this, and we hypothesize that one of… (More)
SPRAAK is a new open source speech recognition package. It is derived from the HMM package that has been developed over the past 15 years at ESAT, KULeuven and which has been in use by a number of… (More)
The objective of this paper is threefold: (1) to provide an extensive review of signal subspace speech enhancement, (2) to derive an upper bound for the performance of these techniques, and (3) to… (More)
In pursuance of better performance, current speech recognition systems tend to use more and more complicated models for both the acoustic and the language component. Cross-word context dependent… (More)
In this paper we introduce the backward N-gram language model (LM) scores as a confidence measure in large vocabulary continuous speech recognition.
In this paper, we describe a method to enhance the readability of the textual output in a large vocabulary continuous speech recognizer when out-of-vocabulary words occur.
In this paper we present two techniques to cover the gap between the true and the estimated clean speech features in the context of Model-Based Feature Enhancement (MBFE) for noise robust speech… (More)