Saurous, Rif A.

Natural TTS Synthesis by Conditioning Wavenet on MEL Spectrogram Predictions

Shen, J., Pang, R., Weiss, R. J., Schuster, M., Jaitly, N., Yang, Z., Chen, Z., Zhang, Y., Wang, Y., Skerrv-Ryan, R., et al. (2018) · 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 2,791

Tacotron: towards end-to-end speech synthesis

Wang, Y., Skerry-Ryan, R. J., Stanton, D., Wu, Y., Weiss, R. J., Jaitly, N., Yang, Z., Xiao, Y., Chen, Z., Bengio, S., et al. (2017) · Interspeech 2017

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 1,746

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Srivastava, A., Rastogi, A., Rao, A., Shoeb, A. A. M., Abid, A., Fisch, A., Brown, A. R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 569

Language Model Cascades

Dohan, D., Xu, W., Lewkowycz, A., Austin, J., Bieber, D., Lopes, R. G., Wu, Y., Michalewski, H., Saurous, R. A., Sohl-dickstein, J., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 22

Sequential Monte Carlo Learning for Time Series Structure Discovery

Saad, F. A., Patton, B. J., Hoffman, M. D., Saurous, R. A., & Mansinghka, V. K. (2023) · arXiv

Gaussian Processes and Bayesian InferenceMarkov Chains and Monte Carlo Methods

Cited by 3

MesaNet: Sequence Modeling by Locally Optimal Test-Time Training

von Oswald, J., Scherrer, N., Kobayashi, S., Versari, L., Yang, S., Mittal, S., Schlegel, M., Maile, K., Schimpf, Y., Sieberling, O., et al. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 0