Hamar, Jarle Bauck and Doddipatla, Rama Sanand and Svendsen, Torbjorn and Sreenivas, Thippur (2013) NON-NEGATIVE DURATIONAL HMM. In: 23rd IEEE International Workshop on Machine Learning for Signal Processing (MLSP), SEP 22-25, 2013, Southampton, ENGLAND.
![]() |
PDF
IEEE_Int_Wor_MLSP_2013.pdf - Published Version Restricted to Registered users only Download (259kB) | Request a copy |
Abstract
Non-negative HMM (N-HMM) has been proposed in the literature as a combination of NMF (non-negative matrix factorisation) and HMM, to model a mixture of non-stationary signals using latent variables. The original formulation of N-HMM does not generalise to unseen data and hence limits its usage in automatic speech recognition (ASR). We propose modifications to the N-HMM formulation to generalise for unseen data and thereby making it suitable for ASR. The modified model is referred to as Non-negative durational HMM(NdHMM). We derive the EM algorithm for estimating the NdHMM parameters and show that the proposed model requires less number of parameters than conventional HMM.
Item Type: | Conference Proceedings |
---|---|
Series.: | IEEE International Workshop on Machine Learning for Signal Processing |
Publisher: | IEEE |
Additional Information: | Copy right for this article belongs to the 23rd IEEE International Workshop on Machine Learning for Signal Processing (MLSP), Southampton, ENGLAND, SEP 22-25, 2013 |
Keywords: | Non-negative matrix factorization;Hidden Markov model;Non-negative HMM; N-HMM;Non-negative durational HMM;NdHMM;Automatic speech recognition;ASR |
Department/Centre: | Division of Electrical Sciences > Electrical Communication Engineering |
Date Deposited: | 24 Aug 2016 10:36 |
Last Modified: | 24 Aug 2016 10:36 |
URI: | http://eprints.iisc.ac.in/id/eprint/54414 |
Actions (login required)
![]() |
View Item |