NDLI: Subspace Gaussian Mixture Models for vectorial HMM-states representation

Content Provider	IEEE Xplore Digital Library
Author	Bouallegue, M. Matrouf, D. Rouvier, M. Linares, G.
Copyright Year	2011
Description	Author affiliation: University of Avignon, LIA, France (Bouallegue, M.; Matrouf, D.; Rouvier, M.; Linares, G.)
Abstract	In this paper we present a vectorial representation of the HMM states that is inspired by the Subspace Gaussian Mixture Models paradigm (SGMM). This vectorial representation of states will make possible a large number of applications, such as HMM-states clustering and graphical visualization. Thanks to this representation, the Hidden Markov Model (HMM) states can be seen as sets of points in multi-dimensional space and then can be studied using statistical data analysis techniques. In this paper, we show how this representation can be obtained and used for tying states of an HHM-based automatic speech recognition system without any use of linguistic or phonetic knowledge. In experiments, this approach achieves significant and stable gain, while conserving the classical approach based on decision trees. We also show how it can be used for graphical visualization, which can be useful in other domains like phonetics or clinical phonetics.
Starting Page	512
Ending Page	516
File Size	140730
Page Count	5
File Format	PDF
ISBN	9781467303651
e-ISBN	9781467303675
e-ISBN	9781467303668
DOI	10.1109/ASRU.2011.6163984
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-11
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Vectors Acoustics Speech recognition Speech Pragmatics Decision trees Subspace Gaussian mixture HMM-state vector representation Acoustic Modelling HMM states clustering
Content Type	Text
Resource Type	Article

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