Output details
15 - General Engineering
Brunel University London
Article title
Analysis and prediction of acoustic speech features from mel-frequency cepstral coefficients in distributed speech recognition architectures
Type
D - Journal article
Title of journal
The Journal of the Acoustical Society of America
Article number
-
Volume number
124
Issue number
6
First page of article
3989
ISSN of journal
00014966
Year of publication
2008
Number of additional authors
2
Additional information
This collaborative work developed methods that enable speech features to be predicted from mel-frequency cepstral coefficient (MFCC) vectors as may be encountered in distributed speech recognition architectures. Experimental results are presented across a range of conditions, such as with speaker-dependent, gender-dependent, and gender-independent constraints, and these show that acoustic speech features can be predicted from MFCC vectors with good accuracy. A comparison is also made against an alternative scheme that substitutes the higher-order MFCCs with acoustic features for transmission.
Interdisciplinary
-
Cross-referral requested
-
Research group
None
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-
