TED-LIUM 3: twice as much data and corpus repartition for experiments on speaker adaptation

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Rousseau, A., Deléglise, P., Estève, Y.: Enhancing the TED-LIUM corpus with selected data for language modeling and more TED talks. In: LREC. pp. 3935–3939 (2014)

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Peddinti, V., Povey, D., Khudanpur, S.: A time delay neural network architecture for efficient modeling of long temporal contexts. In: INTERSPEECH (2015)

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Amodei, D., Ananthanarayanan, S., Anubhai, R., Bai, J., Battenberg, E., Case, C., Casper, o.: Deep speech 2: End-to-end speech recognition in english and mandarin. In: International Conference on Machine Learning. pp. 173–182 (2016)

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Povey, D., Peddinti, V., Galvez, D., Ghahremani, P., Manohar, V., Na, X., Wang, Y., Khudanpur, S.: Purely sequence-trained neural networks for ASR based on lattice-free MMI. In: INTERSPEECH (2016)

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Xu, H., Li, K., Wang, Y., Wang, J., Kang, S., Chen, X., Povey, D., Khudanpur, S.: Neural network language modeling with letter-based features and importance sampling. In: (ICASSP (2017)

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Peddinti, V., Wang, Y., Povey, D., Khudanpur, S.: Low latency acoustic modeling using temporal convolution and LSTMs. IEEE Signal Processing Letters 25

2018

Closest in time.

Povey, D., Cheng, G., Wang, Y., Li, K., Xu, H., Yarmohamadi, M., Khudanpur, S.: Semi-orthogonal low-rank matrix factorization for deep neural networks. In: INTERSPEECH (2018 - submitted)