Output details
11 - Computer Science and Informatics
University of Edinburgh
Article title
Joint Uncertainty Decoding for Noise Robust Subspace Gaussian Mixture Models
Type
D - Journal article
Title of journal
IEEE Transactions on Audio, Speech and Language Processing
Article number
-
Volume number
21
Issue number
9
First page of article
1791
ISSN of journal
1558-7916
Year of publication
2013
Number of additional authors
3
Additional information
<22> Originality: This work develops and evaluates model-based noise compensation for subspace GMM acoustic models which have been demonstrated to extend the state of the art in speech recognition. The originality lies in the computationally feasible approach to JUD-based noise compensation.
Significance: Robustness to noise is the biggest challenge in automatic speech recognition. This work solves some key technical problems in applying model-based noise compensation to subspace GMMs.
Rigour: State-of-the-art experimental results when evaluated on the standard Aurora-4 corpus. Provides detailed derivations for JUD transform estimation with subspace GMMs.
Interdisciplinary
-
Cross-referral requested
-
Research group
D - Institute for Language, Cognition & Computation
Citation count
0
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-
