CUED Publications database

Standalone training of context-dependent deep neural network acoustic models

Zhang, C and Woodland, PC (2014) Standalone training of context-dependent deep neural network acoustic models. In: UNSPECIFIED pp. 5597-5601..

Full text not available from this repository.


Recently, context-dependent (CD) deep neural network (DNN) hidden Markov models (HMMs) have been widely used as acoustic models for speech recognition. However, the standard method to build such models requires target training labels from a system using HMMs with Gaussian mixture model output distributions (GMM-HMMs). In this paper, we introduce a method for training state-of-the-art CD-DNN-HMMs without relying on such a pre-existing system. We achieve this in two steps: build a context-independent (CI) DNN iteratively with word transcriptions, and then cluster the equivalent output distributions of the untied CD-DNN HMM states using the decision tree based state tying approach. Experiments have been performed on the Wall Street Journal corpus and the resulting system gave comparable word error rates (WER) to CD-DNNs built based on GMM-HMM alignments and state-clustering. © 2014 IEEE.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Divisions: Div F > Machine Intelligence
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:01
Last Modified: 22 May 2018 06:59