Unsupervised Bottleneck Features for Low-Resource Query-by-Example Spoken Term Detection

@inproceedings{Chen2016UnsupervisedBF,
  title={Unsupervised Bottleneck Features for Low-Resource Query-by-Example Spoken Term Detection},
  author={Hongjie Chen and Cheung-Chi Leung and Lei Xie and Bin Ma and Haizhou Li},
  booktitle={INTERSPEECH},
  year={2016}
}
We propose a framework which ports Dirichlet Gaussian mixture model (DPGMM) based labels to deep neural network (DNN). The DNN trained using the unsupervised labels is used to extract a low-dimensional unsupervised speech representation, named as unsupervised bottleneck features (uBNFs), which capture considerable information for sound cluster discrimination. We investigate the performance of uBNF in queryby-example spoken term detection (QbE-STD) on the TIMIT English speech corpus. Our uBNF… CONTINUE READING
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Key Quantitative Results

  • With the score fusion of uBNFs and cross-lingual BNFs, we gain about 10% relative improvement in terms of mean average precision (MAP) comparing with the cross-lingual BNFs.

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Acceleration for query-by-example using posteriorgram of deep neural network

2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) • 2017
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