Paper Info
Title
Nonparametric Hidden Markov Models: Principles and Applications to Speech Recognition
Abstract
Continuous-density hidden Markov models (HMM) are a popular approach to the problem of modeling sequential data, e.g. in automatic speech recognition (ASR), off-line handwritten text recognition, and bioinformatics. HMMs rely on strong assumptions on their statistical properties, e.g. the arbitrary parametric assumption on the form of the emission probability density functions (pdfs). This chapter proposes a nonparametric HMM based on connectionist estimates of the emission pdfs, featuring a global gradient-ascent training algorithm over the maximum-likelihood criterion. Robustness to noise may be further increased relying on a soft parameter grouping technique, namely the introduction of adaptive amplitudes of activation functions. Applications to ASR tasks are presented and analyzed, evaluating the behavior of the proposed paradigm and allowing for a comparison with standard HMMs with Gaussian mixtures, as well as with other state-of-the-art neural net/HMM hybrids.
Year
DOI
Venue
2003
10.1007/978-3-540-45216-4_1
Lecture Notes in Computer Science
Keywords
Field
DocType
neural net,probability density function,maximum likelihood,speech recognition,hidden markov model,activation function,automatic speech recognition
Markov process,Pattern recognition,Computer science,Robustness (computer science),Nonparametric statistics,Speech recognition,Gaussian,Parametric statistics,Learning rule,Artificial intelligence,Artificial neural network,Hidden Markov model
Conference
Volume
ISSN
Citations 
2859
0302-9743
1
PageRank 
References 
Authors
0.43
9
1
Name
Order
Citations
PageRank
Edmondo Trentin128629.25