Fourth European Conference on Speech Communication and Technology

Madrid, Spain
September 18-21, 1995

A Minimum Error Training of Garbage Model for Keyword Spotter with Artificially Generated Training Data

Atsushi Nakamura

ATR Interpreting Telecommunications Research Labs., Soraku-Gun, Kyoto, Japan

This paper discusses a method for training a garbage model for a keyword spotter. Conventionally, minimum error training of garbage models, which have been shown to be effective, required a large amount of keyword speech samples as training data. So, on changing a keyword set, a process of collecting a large amount of speech samples has been required. In order to solve this problem, this paper proposes a training method which needs no keyword speech samples, so a keyword set can be changed quickly and economically. Experimental results show the spotting performance can be improved by a training process without raw keyword speech samples.

Full Paper

Bibliographic reference.  Nakamura, Atsushi (1995): "A minimum error training of garbage model for keyword spotter with artificially generated training data", In EUROSPEECH-1995, 1641-1644.