2 code implementations • 28 Oct 2021 • Yao-Yuan Yang, Moto Hira, Zhaoheng Ni, Anjali Chourdia, Artyom Astafurov, Caroline Chen, Ching-Feng Yeh, Christian Puhrsch, David Pollack, Dmitriy Genzel, Donny Greenberg, Edward Z. Yang, Jason Lian, Jay Mahadeokar, Jeff Hwang, Ji Chen, Peter Goldsborough, Prabhat Roy, Sean Narenthiran, Shinji Watanabe, Soumith Chintala, Vincent Quenneville-Bélair, Yangyang Shi
This document describes version 0. 10 of TorchAudio: building blocks for machine learning applications in the audio and speech processing domain.
5 code implementations • LREC 2018 • Tomas Mikolov, Edouard Grave, Piotr Bojanowski, Christian Puhrsch, Armand Joulin
Many Natural Language Processing applications nowadays rely on pre-trained word representations estimated from large text corpora such as news collections, Wikipedia and Web Crawl.
9 code implementations • arXiv 2016 • Ronan Collobert, Christian Puhrsch, Gabriel Synnaeve
This paper presents a simple end-to-end model for speech recognition, combining a convolutional network based acoustic model and a graph decoding.
no code implementations • 29 Sep 2015 • Tom Sercu, Christian Puhrsch, Brian Kingsbury, Yann Lecun
However, CNNs in LVCSR have not kept pace with recent advances in other domains where deeper neural networks provide superior performance.
Ranked #17 on Speech Recognition on Switchboard + Hub500
no code implementations • 20 Jan 2015 • Siddharth Krishna, Christian Puhrsch, Thomas Wies
This is a standard problem in machine learning: given a sample of good and bad points, one is asked to find a classifier that generalizes from the sample and separates the two sets.
10 code implementations • NeurIPS 2014 • David Eigen, Christian Puhrsch, Rob Fergus
Predicting depth is an essential component in understanding the 3D geometry of a scene.