Feature Normalisation for Robust Speech Recognition

• Main Author: Kumar, D. S. Pavan
• Format: Journal Article
• Language: English
• Published: 14-07-2015
• Subjects: Computer Science - Computation and Language; Computer Science - Sound
• DOI: 10.48550/arxiv.1507.04019

Speech recognition system performance degrades in noisy environments. If the
acoustic models are built using features of clean utterances, the features of a
noisy test utterance would be acoustically mismatched with the trained model.
This gives poor likelihoods and poor recognition accuracy. Model adaptation and
feature normalisation are two broad areas that address this problem. While the
former often gives better performance, the latter involves estimation of lesser
number of parameters, making the system feasible for practical implementations.
This research focuses on the efficacies of various subspace, statistical and
stereo based feature normalisation techniques. A subspace projection based
method has been investigated as a standalone and adjunct technique involving
reconstruction of noisy speech features from a precomputed set of clean speech
building-blocks. The building blocks are learned using non-negative matrix
factorisation (NMF) on log-Mel filter bank coefficients, which form a basis for
the clean speech subspace. The work provides a detailed study on how the method
can be incorporated into the extraction process of Mel-frequency cepstral
coefficients. Experimental results show that the new features are robust to
noise, and achieve better results when combined with the existing techniques.
The work also proposes a modification to the training process of SPLICE
algorithm for noise robust speech recognition. It is based on feature
correlations, and enables this stereo-based algorithm to improve the
performance in all noise conditions, especially in unseen cases. Further, the
modified framework is extended to work for non-stereo datasets where clean and
noisy training utterances, but not stereo counterparts, are required. An
MLLR-based computationally efficient run-time noise adaptation method in SPLICE
framework has been proposed.