Investigating stranded GMM for improving automatic speech recognition

Abstract

This paper investigates recently proposed Stranded Gaussian Mixture acoustic Model (SGMM) for Automatic Speech Recognition (ASR). This model extends conventional hidden Markov model (HMM-GMM) by explicitly introducing dependencies between components of the observation Gaussian mixture densities. The main objective of the paper is to experimentally study, how useful SGMM can be for dealing with data, which contains different sources of acoustic variability. First studied sources of variability are age and gender in quiet environment (TIdigits task including child speech). Second, the SGMM modeling is applied on data produced by different speakers and corrupted by non-stationary noise (CHiME 2013 challenge data). Finally, SGMM is applied on the same noisy data, but after performing speech enhancement (i.e., the remaining variability mostly comes from residual noise and different speakers). Although SGMM was originally proposed for robust speech recognition of noisy data, in this work it was found, that the model is more efficient for handling speaker variability in quiet environment.

Component structuring and trajectory modeling for speech recognition

“…Each state density is modeled by 32 Gaussian components. The front-end is the same in all experiments described in the paper, and it consists of 13 Training on adult data provides the best results for adult speakers, but shows a weak performance on child speech. When child data are included in the training set, the performance improves on child, but degrades on adult subset.…”

“…Another way of using classstructured GMM is to replace class-dependent mixture weights by Mixture Transition Matrices (MTMs) of the Stranded Gaussian Mixture Model (SGMM). SGMM is similar to conditional Gaussian model [11], which was recently extended, reformulated and investigated for robust ASR [12] and investigated for child data and non-stationary noise conditions [13]. MTMs explicitly define the dependencies between the components of the adjacent Gaussian mixture observation densities.…”