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Automatic detection of phoneme or word-like units is one of the core objectives in zero-resource speech processing.
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L. Badino, C. Canevari, L. Fadiga, and G. Metta, “An auto-encoder based approach to unsupervised learning of subword units,” in Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on
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H. Kamper, A. Jansen, and S. Goldwater, “A segmental framework for fully-unsupervised large-vocabulary speech recognition,” Computer Speech & Language
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2019
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J. Chorowski, R. J. Weiss, S. Bengio, and A. van den Oord, “Unsupervised speech representation learning using wavenet autoencoders,” IEEE/ACM transactions on audio, speech, and language processing
2019
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2019
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S. Bhati, S. Nayak, K. S. R. Murty, and N. Dehak, “Unsupervised acoustic segmentation and clustering using siamese network embeddings,” Proc. Interspeech 2019
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S. Bhati, S. Nayak, and K. S. R. Murty, “Unsupervised speech signal to symbol transformation for zero resource speech applications,” Proc. Interspeech 2017
2017
Cited alongside, same era.
H. Kamper, K. Livescu, and S. Goldwater, “An embedded segmental k-means model for unsupervised segmentation and clustering of speech,” in 2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)
2017
Cited alongside, same era.
2017
Cited alongside, same era.
2017
Cited alongside, same era.
S. Bhati, H. Kamper, and K. S. R. Murty, “Phoneme based embedded segmental k-means for unsupervised term discovery,” in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2019
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2020
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2020
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2020
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