Spoken keyword detection using autoassociative neural networks

摘要

Spoken keywords detection is essential to organize efficiently lots of hours of audio contents such as meetings, radio news, etc. These systems are developed with the purpose of indexing large audio databases or of detecting keywords in continuous speech streams. This paper addresses a new approach to spoken keyword detection using Autoassociative Neural Networks (AANN). The proposed work concerns the use of the distribution capturing ability of the Autoassociative neural network (AANN) for spoken keyword detection. It involves sliding a frame-based keyword template along the speech signal and using confidence score obtained from the normalized squared error of AANN to efficiently search for a match. This work formulates a new spoken keyword detection algorithm. The experimental results show that the proposed approach competes with the keyword detection methods reported in the literature and it is an alternative method to the existing key word detection methods.