Abstract
Automatic speech recognition (ASR) is an area where the task is to assign the correct phoneme or word sequence to an utterance. The idea behind the ASR segment-based approach is to treat one phoneme as a whole unit in every respect, in contrast with the frame-based approach where it is divided into equal-sized, smaller chunks. Doing this has many advantages, but also gives rise to some new problems. One of these is the detection of potential bounds between phones, which has an effect on both the recognition accuracy and the speed of the speech recognition system. In this paper we present three ways of boundary detection: first two simple algorithms are tested, then we will concentrate on our novel method which incorporates a spiking neuron. On examining the test results we find that the latter algorithm indeed proves successful: we were able to speed up the recognition process by 35.72% while also slightly improving the recognition performance.
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Gosztolya, G., Tóth, L. (2008). Detection of Phoneme Boundaries Using Spiking Neurons. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2008. ICAISC 2008. Lecture Notes in Computer Science(), vol 5097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69731-2_75
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DOI: https://doi.org/10.1007/978-3-540-69731-2_75
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