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Morph-based language modeling has been efficiently applied in improving the accuracy of Large-Vocabulary Continuous Speech Recognition (LVCSR) systems ± especially in morphologically rich languages. However, the rate of improvements varies greatly and the underlying principles have been only superficially studied. Having a method that can predict the(More)
Under real-life conditions several factors may be present that make the automatic recognition of speech difficult. The most obvious examples are background noise, peculiarities of the speaker's voice, sloppy articulation and strong emotional load. These all pose difficult problems for robust speech recognition, but it is not exactly clear how much each(More)
In this paper, the application of LVCSR (Large Vocabulary Continuous Speech Recognition) technology is investigated for real-time, resource-limited broadcast close captioning. The work focuses on transcribing live broadcast conversation speech to make such programs accessible to deaf viewers. Due to computational limitations, real time factor (RTF) and(More)
This paper summarizes our recent efforts made to transcribe real-life Call Center conversations automatically with respect to non-verbal acoustic events, as well. Future Call Centers – as cognitive infocom systems – must respond automatically not only for well formed utterances but also for spontaneous and non-word speaker manifestations and must be robust(More)
This paper introduces our work and results related to a multiple language continuous speech recognition task. The aim was to design a system that introduces tolerable amount of recognition errors for point of interest words in voice navigational queries even in the presence of real-life traffic noise. Additional challenges were that no task-specific(More)
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