Weekly Research Newsletter - KIIT

By Priyansi, Junaid Rahim and Biswaroop Bhattacharjee

Weekly Research Newsletter - KIIT - Issue #13

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Weekly Research Newsletter - KIIT
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Weekly Research Newsletter - KIIT - Issue #13
29th May 2021,
We are excited to share this week’s picks for the research newsletter. We hope you’ll enjoy reading them over the weekend.

PyTouch: A Machine Learning Library for Touch Processing
By Mike Lambeta, Huazhe Xu, Jingwei Xu, Po-Wei Chou, Shaoxiong Wang, Trevor Darrell, Roberto Calandra
With the increased availability of rich tactile sensors, there is an equally proportional need for open-source and integrated software capable of efficiently and effectively processing raw touch measurements into high-level signals that can be used for control and decision-making. In this paper, we present PyTouch – the first machine learning library dedicated to the processing of touch sensing signals. PyTouch, is designed to be modular, easy-to-use and provides state-of-the-art touch processing capabilities as a service with the goal of unifying the tactile sensing community by providing a library for building scalable, proven, and performance-validated modules over which applications and research can be built upon. We evaluate PyTouch on real-world data from several tactile sensors on touch processing tasks such as touch detection, slip and object pose estimations. PyTouch is open-sourced at this https URL .
Unsupervised Speech Recognition
By Alexei Baevski, Wei-Ning Hsu, Alexis Conneau, Michael Auli
Despite rapid progress in the recent past, current speech recognition technology still requires labeled training data. This limits this technology to a small fraction of the languages spoken around the globe. This paper describes wav2vec-U, short for wav2vec Unsupervised, a method to train speech recognition models without any labeled data. We leverage self-supervised speech representations to segment unlabeled audio and learn a mapping from these representations to phonemes via adversarial training. The right representations are key to the success of our method. Compared to the best previous unsupervised work, wav2vec-U reduces the phoneme error rate on the TIMIT benchmark from 26.1 to 11.3. On the larger English Librispeech benchmark, wav2vec-U achieves a word error rate of 5.9 on test-other, rivaling some of the best published systems trained on 960 hours of labeled data from only two years ago. We also experiment on nine other languages, including low-resource languages such as Kyrgyz, Swahili and Tatar.
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Priyansi, Junaid Rahim and Biswaroop Bhattacharjee

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