Abstract
The hidden Markov model (HMM) technique has become very popular in the signal and data processing areas during the last 10 years. It is not easy, however, to understand its complex nature that is ‘hidden’ behind a ‘veil’ of two probability functions, one associated with the given space of data parameters and the other with the temporal data flow. Our system, named Visual Markov, aims at removing the veil by visualizing the continuous density HMM and displaying its individual states. Moreover, it is able to show the iterative process of HMM training, step after step. In a similar way, also the HMM based classification can be presented. The system is a highly illustrative tool that is well suited both for research and teaching purposes. In the article, we demostrate its application in the speech recognition domain.
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Hajek, D., Nouza, J. (1996). Unhiding Hidden Markov Models by Their Visualization (Application in Speech Processing). In: Göbel, M., David, J., Slavik, P., van Wijk, J.J. (eds) Virtual Environments and Scientific Visualization ’96. Eurographics. Springer, Vienna. https://doi.org/10.1007/978-3-7091-7488-3_28
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DOI: https://doi.org/10.1007/978-3-7091-7488-3_28
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-82886-1
Online ISBN: 978-3-7091-7488-3
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