DocumentCode
2020784
Title
An optimal learning method for minimizing spotting errors
Author
Komori, Takashi ; Katagiri, Shigeru
Author_Institution
ATR Auditory & Visual Perception Res. Lab., Soraku-gun, Kyoto, Japan
Volume
2
fYear
1993
fDate
27-30 April 1993
Firstpage
271
Abstract
A novel design method for word spotting, called MSPE (minimum spotting error), is proposed which guarantees a minimum spotting error situation in a probabilistic sense through MCE/GPD (minimum classification error/generalized probabilistic descent) optimization. MPSE makes it possible to train all trainable parameters consistently; this feature implies an innovative departure from conventional, heuristic approaches to spotter design. Experimental results have demonstrated a very high utilization potential for MSPE.<>
Keywords
errors; learning (artificial intelligence); minimisation; neural nets; speech recognition; generalized probabilistic descent; minimum spotting error; optimal learning method; trainable parameters; utilization potential; word spotting;
fLanguage
English
Publisher
ieee
Conference_Titel
Acoustics, Speech, and Signal Processing, 1993. ICASSP-93., 1993 IEEE International Conference on
Conference_Location
Minneapolis, MN, USA
ISSN
1520-6149
Print_ISBN
0-7803-7402-9
Type
conf
DOI
10.1109/ICASSP.1993.319288
Filename
319288
Link To Document