Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
AANN: an alternative to GMM for pattern recognition
Neural Networks
Artificial Neural Networks
A comparison of grapheme and phoneme-based units for Spanish spoken term detection
Speech Communication
Speaker diarization using autoassociative neural networks
Engineering Applications of Artificial Intelligence
Point process models for spotting keywords in continuous speech
IEEE Transactions on Audio, Speech, and Language Processing
Learning in multilayered networks used as autoassociators
IEEE Transactions on Neural Networks
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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.