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In this paper, a DNN based keyword spotting framework, that utilizes both spectral as well as prosodic information present in the speech signal, is proposed. A DNN is first trained to learn a set of hierarchical non-linear transformation parameters that project the original spectral and prosodic feature vectors onto a feature space where the distance between similar syllable pairs is small and between dissimilar syllable pairs is large. These transformed features are then fused using an attention based long short-term memory (LSTM) network. As a side result, a deep denoising autoencoder based fine-tuning technique is used to improve the performance of sequence predictions.

Added on December 19, 2018

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  • Contributed by : Individual
  • Product Type : Research Paper
  • License Type : Freeware
  • System Requirement : Not Applicable
  • Author : Laxmi Pandey, Karan Nathwani2
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