Digital Signal Processing Reference
In-Depth Information
ergonomic opportunities of speech driven user interfaces. Efficient dialogs
lead the operator automatically to the desired function. The goal is a self-
explaining composition of devices with a managing intelligence, which
interacts with the internal states of the devices as well as with the user via
speech and buttons. A well-designed interface in the car minimizes
distraction from traffic during device operation and makes the study of user
manuals unnecessary.
REFERENCES
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T. Rudolph,
Evolutionary Optimization of Fast Command Recognizers,
(in German), Phd
thesis, Dresden University of Technology, 1998.
A. Noll A. Paesler H. Ney, D. Mergel, “Data-driven search organisation for continuous
speech recognition,”
IEEE Trans. Signal Processing,
vol. 40, pp. 272-281, 1992.
U. Koloska T. Richter R. Petrick D. Hirschfeld, J. Bechstein, “Development steps of a
hardware recognizer with minimal footprint”, (in German),
Proc. 13th Conf. on Electronic
Speech Signal Processing (ESSV), Dresden,
pp. 182-189, 2002.
W. Hess P. Vary, U. Heute,
Digital Speech Signal Processing,
(in German), Teubner,
Stuttgart, 1998.
G. Ruske:
Automatische Spracherkennung - Methoden der Klassifikation und
Merkmalsextraktion,
München: Oldenbourg Verlag, 1988.
Fukunaga:
Introduction to Statistical Pattern Recognition,
San Diego: Academic Press,
1990.
ETSI EN 301, V7.1.1:
Voice activity detector (VAD) for AdaptiveMulti-Rate (AMR)
speech traffic channels
, General description (GSM 06.94 version 7.1.1 Release 1998)
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