1. What are the contributions in this paper?
It is the purpose of this tutorial paper to give an introduction to the theory of Markov models, and to illustrate how they have been applied to problems in speech recognition.
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Figures
Figure 6. Illustration of the computation required for the calculation of the joint event that the system is in state qi at time t. and state qj at ti~e t + 1. This event occurs with probability atU) (which accounts for the path terminating in state qi at time t). times Bij bj W t+ 1) (which accounts for the local transition from state qj). times {3t+1(j) (which accounts for the path being in state j at time t + 1 and then being unconstrained until the end of the observation sequence). 
Figure 6. Illustration of the computation required for the calculation of the joint event that the system is in state qi at time t. and state qj at ti~e t + 1. This event occurs with probability atU) (which accounts for the path terminating in state qi at time t). times Bij bj W t+ 1) (which accounts for the local transition from state qj). times {3t+1(j) (which accounts for the path being in state j at time t + 1 and then being unconstrained until the end of the observation sequence).
Citations
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De l'identification de structure de réseaux bayésiens à la reconnaissance de formes à partir d'informations complètes ou incomplètes
Olivier François
- 01 Jan 2006
TL;DR: Cette methode est ensuite adaptee a la problematique plus specifique de the classification and permet d'apprendre efficacement et en toute generalite un classifieur de Bayes Naif augmente.
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A statistical causal model for the assessment of dysarthric speech and the utility of computer-based speech recognition
Bon K. Sy,D.M. Horowitz +1 more
TL;DR: It is a difficult task to relate a speech impairment rating with speech recognition accuracy, and a statistical causal model is proposed that is very appealing in its structure to support inference and thus can be applied to perform various assessments such as the success of automatic recognition of dysarthric speech.
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Extracting Knowledge from Big Data for Sustainability: A Comparison of Machine Learning Techniques
TL;DR: Different big data analysis machine learning methods are used to extracting knowledge from data to find out fertilizer recommendation classes on behalf of present soil nutrition composition, and it is concluded that the SGD outperforms other approaches.
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Non-negative component parts of sound for classification
Yong-Choon Cho,Seungjin Choi,Sung-Yang Bang +2 more
- 14 Dec 2003
TL;DR: Experimental results show that NMF- based features improve the performance of sound classification over ICA-based features, and methods of feature extraction from spectro-temporal sounds using the NMF in the absence or presence of noise are explained.
References
The viterbi algorithm
Jr. G.D. Forney
- 01 Mar 1973
TL;DR: This paper gives a tutorial exposition of the Viterbi algorithm and of how it is implemented and analyzed, and increasing use of the algorithm in a widening variety of areas is foreseen.
6.5K
Linear Prediction of Speech
J. Markel,Augustine H. Gray +1 more
TL;DR: Linear prediction of speech is a new area in speech processing that has evolved in the past ten years.
2.1K
An inequality with applications to statistical estimation for probabilistic functions of Markov processes and to a model for ecology
Leonard E. Baum,J. A. Eagon +1 more
TL;DR: In this paper, a polynomial with nonnegative coefficients homogeneous of degree d in its variables is shown to be polynomially homogeneous unless 3(3(x))>P(x), where 3(x)=x.
An introduction to the application of the theory of probabilistic functions of a Markov process to automatic speech recognition
TL;DR: This paper presents several of the salient theoretical and practical issues associated with modeling a speech signal as a probabilistic function of a (hidden) Markov chain, and focuses on a particular class of Markov models, which are especially appropriate for isolated word recognition.
1.1K