NDLI: Spectral learning with non negative probabilities for finite state automaton

Content Provider	IEEE Xplore Digital Library
Author	Glaude, Hadrien Enderli, Cyrille Pietquin, Olivier
Copyright Year	2015
Description	Author affiliation: Thales Airborne Systems, Elancourt, France (Enderli, Cyrille) \|\| Univ. Lille, CRIStAL, UMR 9189, SequeL Team, Villeneuve d'Ascq, France (Glaude, Hadrien; Pietquin, Olivier)
Abstract	Probabilistic Finite Automaton (PFA), Probabilistic Finite State Transducers (PFST) and Hidden Markov Models (HMM) are widely used in Automatic Speech Recognition (ASR), Text-to-Speech (TTS) systems and Part Of Speech (POS) tagging for language modeling. Traditionally, unsupervised learning of these latent variable models is done by Expectation-Maximization (EM)-like algorithms, as the Baum-Welch algorithm. In a recent alternative line of work, learning algorithms based on spectral properties of some low order moments matrices or tensors were proposed. In comparison to EM, they are orders of magnitude faster and come with theoretical convergence guarantees. However, returned models are not ensured to compute proper distributions. They often return negative values that do not sum to one, limiting their applicability and preventing them to serve as an initialization to EM-like algorithms. In this paper, we propose a new spectral algorithm able to learn a large range of models constrained to return proper distributions. We assess its performances on synthetic problems from the PAutomaC challenge and real datasets extracted from Wikipedia. Experiments show that it outperforms previous spectral approaches as well as the Baum-Welch algorithm with random restarts, in addition to serve as an efficient initialization step to EM-like algorithms.
Starting Page	71
Ending Page	77
File Size	290947
Page Count	7
File Format	PDF
e-ISBN	9781479972913
DOI	10.1109/ASRU.2015.7404776
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-12-13
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Learning automata Probabilistic logic Method of moments Algorithm design and analysis Approximation algorithms Computational modeling non-negative matrix factorization Baum-welch learning automata spectral learning language models
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Spectral learning with proper probabilities for finite state automation

Learning hidden markov models using non-negative matrix factorization (2008)

Non-negative matrix factorization for parameter estimation in hidden markov models.

Refined learning of hidden Markov models with a modified Baum-Welch algorithm and informative components

Problem of secondary structure prediction minimize by using the Baum Walch algorithm

A spectral algorithm for learning hidden markov models (2009)

Passively Learning Finite Automata (1996)

A Reevaluation and Benchmark of Hidden Markov Models

Probabilistic Arithmetic Automata and Their Applications

Spectral learning with non negative probabilities for finite state automaton

Similar Documents

Spectral learning with proper probabilities for finite state automation

Learning hidden markov models using non-negative matrix factorization (2008)

Non-negative matrix factorization for parameter estimation in hidden markov models.

Refined learning of hidden Markov models with a modified Baum-Welch algorithm and informative components

Problem of secondary structure prediction minimize by using the Baum Walch algorithm

A spectral algorithm for learning hidden markov models (2009)

Passively Learning Finite Automata (1996)

A Reevaluation and Benchmark of Hidden Markov Models

Probabilistic Arithmetic Automata and Their Applications

Spectral learning with non negative probabilities for finite state automaton