Proceedings ArticleDOI
Use of semi-Markov models for speaker-independent phoneme recognition
N. Ratnayake,M. Savic,J. Sorensen +2 more
- Vol. 1, pp 565-568
TLDR
Preliminary tests conducted using only the linear prediction coding (LPC) cepstrum as features have shown that the use of HSMM increased the phoneme recognition accuracy to 53.7% from the 48.4% obtained using an HMM.Abstract:
Hidden Markov models (HMMs) have been used to model speech in many areas of speech processing. One characteristic of the HMM is that the probability of time spent in a particular state, or state occupancy, is geometrically distributed. This, however, becomes a serious limitation and results in inaccurate modeling when the HMMs are used for phoneme recognition. The authors use hidden semi-Markov models (HSMM) to overcome the above limitation. Semi-Markov models are a more general class of Markov chains in which the state occupancy can be explicitly modeled by an arbitrary probability mass distribution. The authors use non-parametric distributions to describe the state occupancies instead of parametric distributions such as gamma. Poisson or binomial, as analysis of actual data shows that the duration of some phonemes could not be approximated by any of the above. Preliminary tests conducted using only the linear prediction coding (LPC) cepstrum as features have shown that the use of HSMM increased the phoneme recognition accuracy to 53.7% from the 48.4% obtained using an HMM. >read more
Citations
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Journal ArticleDOI
Hidden semi-Markov models
TL;DR: An overview of HSMMs is presented, including modelling, inference, estimation, implementation and applications, which has been applied in thirty scientific and engineering areas, including speech recognition/synthesis, human activity recognition/prediction, handwriting recognition, functional MRI brain mapping, and network anomaly detection.
Book
Hidden Semi-Markov Models: Theory, Algorithms and Applications
TL;DR: How to master the basic techniques needed for using HSMMs and how to apply them are shown, as well as a description of applications in various areas including, Human Activity Recognition, Handwriting recognition, Network Traffic Characterization and Anomaly Detection, and Functional MRI Brain Mapping.
Dissertation
HMM-based Speech Synthesis Using an Acoustic Glottal Source Model
TL;DR: A new approach to using an acoustic glottal source model in HMM-based synthesisers to improve speech quality and parametric flexibility to better model and transform voice characteristics.
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Sticky Hidden Markov Modeling of Comparative Genomic Hybridization
TL;DR: A sticky hidden Markov model with a Dirichlet distribution (DD) prior is developed, motivated by the problem of analyzing comparative genomic hybridization (CGH) data, and the form of the proposed hierarchical model allows efficient variational Bayesian (VB) inference.
Optimal Curve Fitting of Speech Signal for Disabled Children
TL;DR: In this paper, the amplitude profile of sampled speech data were fitted by employing sum of sine functions with a confidence level more than 90% and amplitude correlation technique is applied between original speech signal samples of normal and pathological subjects and correlation technique also applied between the curve fit constant values for normal or pathological subjects.
References
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Journal ArticleDOI
Speaker-independent phone recognition using hidden Markov models
Kai-Fu Lee,H.-W. Hon +1 more
TL;DR: The authors introduce the co-occurrence smoothing algorithm, which enables accurate recognition even with very limited training data, and can be used as benchmarks to evaluate future systems.
Journal ArticleDOI
Continuously variable duration hidden Markov models for automatic speech recognition
TL;DR: The solution proposed here is to replace the probability distributions of duration with continuous probability density functions to form a continuously variable duration hidden Markov model (CVDHMM) which is ideally suited to specification of the durational density.
Journal ArticleDOI
On the application of vector quantization and hidden Markov models to speaker-independent, isolated word recognition
TL;DR: This paper presents an approach to speaker-independent, isolated word recognition in which the well-known techniques of vector quantization and hidden Markov modeling are combined with a linear predictive coding analysis front end in the framework of a standard statistical pattern recognition model.
Journal ArticleDOI
Recognition of isolated digits using hidden Markov models with continuous mixture densities
TL;DR: This paper extends previous work on isolated-word recognition based on hidden Markov models by replacing the discrete symbol representation of the speech signal with a continuous Gaussian mixture density, thereby eliminating the inherent quantization error introduced by the discrete representation.
Proceedings ArticleDOI
Explicit modelling of state occupancy in hidden Markov models for automatic speech recognition
Martin J. Russell,Roger K. Moore +1 more
TL;DR: Results have been presented which show that these semi-Markov models provide an appropriate framework for modelling durational structure and can lead to significant improvements in recognition accuracy.