ZRC Series Resources Full Bibliography

Full Bibliography

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Adda, Stücker, Adda-Decker, Ambouroue, Besacier, Blachon, Bonneau-Maynard, Godard, Hamlaoui, Idiatov, Kouarata, Lamel, Makasso, Rialland, Van de Velde, Yvon & Zerbian (2016): Adda, G., Stücker, S., Adda-Decker, M., Ambouroue, O., Besacier, L., Blachon, D., Bonneau-Maynard, H., Godard, P., Hamlaoui, F., Idiatov, D., Kouarata, G., Lamel, L., Makasso, E., Rialland, A., Van de Velde, M., Yvon, F. & Zerbian, S. (2016). Breaking the unwritten kanguage barrier: The Bulb project.
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Alishahi, Barking & Chrupała (2017): Alishahi, A., Barking, M. & Chrupała, G. (2017). Encoding of phonology in a recurrent neural model of grounded speech.
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Ansari, Kumar, Singh & Ganapathy (2017): Ansari, T., Kumar, R., Singh, S. & Ganapathy, S. (2017). Deep learning methods for unsupervised acoustic modeling—leap submission to zerospeech challenge 2017. IEEE.
Jansen & Van Durme (2011): Jansen, A. & Van Durme, B. (2011). Efficient spoken term discovery using randomized algorithms. IEEE.
Badino, Canevari, Fadiga & Metta (2014): Badino, L., Canevari, C., Fadiga, L. & Metta, G. (2014). An Auto-encoder based approach to unsupervised learning of subword units.
Baevski, Schneider & Auli (2020): Baevski, A., Schneider, S. & Auli, M. (2020). Vq-wav2vec: Self-supervised learning of discrete speech representations. Retrieved from https://openreview.net/forum?id=rylwJxrYDS
Hannun, Case, Casper, Catanzaro, Diamos, Elsen, Prenger, Satheesh, Sengupta, Coates & (2014): Hannun, A., Case, C., Casper, J., Catanzaro, B., Diamos, G., Elsen, E., Prenger, R., Satheesh, S., Sengupta, S., Coates, A. & (2014). Deep speech: Scaling up end-to-end speech recognition. arXiv preprint arXiv:1412.5567.
Bengio, Ducharme, Vincent & Jauvin (2003): Bengio, Y., Ducharme, R., Vincent, P. & Jauvin, C. (2003). A neural probabilistic language model. JMLR.
Besacier, Zhou & Gao (2006): Besacier, L., Zhou, B. & Gao, Y. (2006). Towards speech translation of non written languages.
Warstadt, Parrish, Liu, Mohananey, Peng, Wang & Bowman (2019): Warstadt, A., Parrish, A., Liu, H., Mohananey, A., Peng, W., Wang, S. & Bowman, S. (2019). BLiMP: A benchmark of linguistic minimal pairs for english. arXiv preprint arXiv:1912.00582.
Peng & Harwath (2022): Peng, P. & Harwath, D. (2022). Self-supervised representation learning for speech using visual grounding and masked language modeling. arXiv preprint arXiv:2202.03543.
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Chung & Glass (2018): Chung, Y. & Glass, J. (2018). Speech2vec: A sequence-to-sequence framework for learning word embeddings from speech. arXiv preprint arXiv:1803.08976.
Chung, Hsu, Tang & Glass (2019): Chung, Y., Hsu, W., Tang, H. & Glass, J. (2019). An unsupervised autoregressive model for speech representation learning. arXiv preprint arXiv:1904.03240.
Chung, Hsu, Tang & Glass (2019): Chung, Y., Hsu, W., Tang, H. & Glass, J. (2019). An unsupervised autoregressive model for speech representation learning. Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH. 146–150. https://doi.org/10.21437/Interspeech.2019-1473
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Kharitonov, Lee, Polyak, Adi, Copet, Lakhotia, Nguyen, Rivière, Mohamed, Dupoux & (2021): Kharitonov, E., Lee, A., Polyak, A., Adi, Y., Copet, J., Lakhotia, K., Nguyen, T., Rivière, M., Mohamed, A., Dupoux, E. & (2021). Text-free prosody-aware generative spoken language modeling. arXiv preprint arXiv:2109.03264.
Heck, Sakti & Nakamura (2016): Heck, M., Sakti, S. & Nakamura, S. (2016). Unsupervised linear discriminant analysis for supporting DPGMM clustering in the zero resource scenario. Procedia Computer Science, 81. 73–79.
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Heck, Sakti & Nakamura (2017): Heck, M., Sakti, S. & Nakamura, S. (2017). Feature optimized DPGMM clustering for unsupervised subword modeling: A contribution to zerospeech 2017. IEEE.
Shibata, Kato, Shinozaki & Watanabet (2017): Shibata, H., Kato, T., Shinozaki, T. & Watanabet, S. (2017). Composite embedding systems for ZeroSpeech2017 Track1. IEEE.
Chorowski, Weiss, Bengio & Van Den Oord (2019): Chorowski, J., Weiss, R., Bengio, S. & Van Den Oord, A. (2019). Unsupervised speech representation learning using wavenet autoencoders. IEEE/ACM transactions on audio, speech, and language processing, 27(12). 2041–2053.
Kamper, Livescu & Goldwater (2017): Kamper, H., Livescu, K. & Goldwater, S. (2017). An embedded segmental k-means model for unsupervised segmentation and clustering of speech. IEEE.
Hsu, Harwath & Glass (2019): Hsu, W., Harwath, D. & Glass, J. (2019). Transfer learning from audio-visual grounding to speech recognition. arXiv preprint arXiv:1907.04355.
Chung & Glass (2019): Chung, Y. & Glass, J. (2019). Generative pre-training for speech with autoregressive predictive coding. arXiv preprint arXiv:1910.12607.
Millet, Chitoran & Dunbar (2021): Millet, J., Chitoran, I. & Dunbar, E. (2021). Predicting non-native speech perception using the perceptual assimilation model and state-of-the-art acoustic models.
Warstadt, Singh & Bowman (2018): Warstadt, A., Singh, A. & Bowman, S. (2018). Neural network acceptability judgments. arXiv preprint 1805.12471.
Dai, Yang, Yang, Cohen, Carbonell, Le & Salakhutdinov (2019): Dai, Z., Yang, Z., Yang, Y., Cohen, W., Carbonell, J., Le, Q. & Salakhutdinov, R. (2019). Transformer-xl: Attentive language models beyond a fixed-length context. arXiv preprint 1901.02860.
Räsänen, Doyle & Frank (2015): Räsänen, O., Doyle, G. & Frank, M. (2015). Unsupervised word discovery from speech using automatic segmentation into syllable-like units.
Räsänen & Blandón (2020): Räsänen, O. & Blandón, M. (2020). Unsupervised discovery of recurring speech patterns using probabilistic adaptive metrics. arXiv preprint arXiv:2008.00731.
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Tjandra, Sakti & Nakamura (2017): Tjandra, A., Sakti, S. & Nakamura, S. (2017). Listening while speaking: Speech chain by deep learning.
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Gao, Singh & Raj (2018): Gao, Y., Singh, R. & Raj, B. (2018). Voice impersonation using generative adversarial networks. IEEE.
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