Non-negative durational HMM

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Jarle Bauck Hamar, Doddipatla Rama Sanand, Torbjørn Svendsen, Thippur Sreenivas

Published: 01 Jan 2013, Last Modified: 04 Nov 2025MLSP 2013EveryoneRevisionsBibTeXCC BY-SA 4.0
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.