We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler. wav2vec 2.0 masks the speech input in the latent space and solves a contrastive task defined over a quantization of the latent representations which are jointly learned. Experiments using all labeled data of Librispeech achieve 1.8/3.3 WER on the clean/other test sets. When lowering the amount of labeled data to one hour, wav2vec 2.0 outperforms the previous state of the art on the 100 hour subset while using 100 times less labeled data. Using just ten minutes of labeled data and pre-training on 53k hours of unlabeled data still achieves 4.8/8.2 WER. This demonstrates the feasibility of speech recognition with limited amounts of labeled data.

/pdf/wav2vec-2-0-a-framework-for-self-supervised-learning-of-445ovxj74v.pdf

wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations

Humans view the world through many sensory channels, e.g., the long-wavelength light channel, viewed by the left eye, or the high-frequency vibrations channel, heard by the right ear. Each view is noisy and incomplete, but important factors, such as physics, geometry, and semantics, tend to be shared between all views (e.g., a "dog" can be seen, heard, and felt). We investigate the classic hypothesis that a powerful representation is one that models view-invariant factors. We study this hypothesis under the framework of multiview contrastive learning, where we learn a representation that aims to maximize mutual information between different views of the same scene but is otherwise compact. Our approach scales to any number of views, and is view-agnostic. We analyze key properties of the approach that make it work, finding that the contrastive loss outperforms a popular alternative based on cross-view prediction, and that the more views we learn from, the better the resulting representation captures underlying scene semantics. Our approach achieves state-of-the-art results on image and video unsupervised learning benchmarks. Code is released at: this http URL.

Contrastive Multiview Coding

/pdf/contrastive-multiview-coding-35vpbvx07b.pdf

The field of defense strategies against adversarial attacks has significantly grown over the last years, but progress is hampered as the evaluation of adversarial defenses is often insufficient and thus gives a wrong impression of robustness. Many promising defenses could be broken later on, making it difficult to identify the state-of-the-art. Frequent pitfalls in the evaluation are improper tuning of hyperparameters of the attacks, gradient obfuscation or masking. In this paper we first propose two extensions of the PGD-attack overcoming failures due to suboptimal step size and problems of the objective function. We then combine our novel attacks with two complementary existing ones to form a parameter-free, computationally affordable and user-independent ensemble of attacks to test adversarial robustness. We apply our ensemble to over 50 models from papers published at recent top machine learning and computer vision venues. In all except one of the cases we achieve lower robust test accuracy than reported in these papers, often by more than $10\%$, identifying several broken defenses.

Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks.

Adversarial examples have attracted significant attention in machine learning, but the reasons for their existence and pervasiveness remain unclear. We demonstrate that adversarial examples can be directly attributed to the presence of non-robust features: features derived from patterns in the data distribution that are highly predictive, yet brittle and incomprehensible to humans. After capturing these features within a theoretical framework, we establish their widespread existence in standard datasets. Finally, we present a simple setting where we can rigorously tie the phenomena we observe in practice to a misalignment between the (human-specified) notion of robustness and the inherent geometry of the data.

pdf/a-discussion-of-adversarial-examples-are-not-bugs-they-are-ru0nblvmeh.pdf

A Discussion of 'Adversarial Examples Are Not Bugs, They Are Features'

advertorch is a toolbox for adversarial robustness research. It contains various implementations for attacks, defenses and robust training methods. advertorch is built on PyTorch (Paszke et al., 2017), and leverages the advantages of the dynamic computational graph to provide concise and efficient reference implementations. The code is licensed under the LGPL license and is open sourced at this https URL .

advertorch v0.1: An Adversarial Robustness Toolbox based on PyTorch.

Unsupervised speech representation learning has shown remarkable success at finding representations that correlate with phonetic structures and improve downstream speech recognition performance. However, most research has been focused on evaluating the representations in terms of their ability to improve the performance of speech recognition systems on read English (e.g. Wall Street Journal and LibriSpeech). This evaluation methodology overlooks two important desiderata that speech representations should have: robustness to domain shifts and transferability to other languages. In this paper we learn representations from up to 8000 hours of diverse and noisy speech data and evaluate the representations by looking at their robustness to domain shifts and their ability to improve recognition performance in many languages. We find that our representations confer significant robustness advantages to the resulting recognition systems: we see significant improvements in out-of-domain transfer relative to baseline feature sets and the features likewise provide improvements in 25 phonetically diverse languages.

/pdf/learning-robust-and-multilingual-speech-representations-25ikapkkue.pdf

Learning Robust and Multilingual Speech Representations

Unsupervised speech representation learning has shown remarkable success at finding representations that correlate with phonetic structures and improve downstream speech recognition performance. However, most research has been focused on evaluating the representations in terms of their ability to improve the performance of speech recognition systems on read English (e.g. Wall Street Journal and LibriSpeech). This evaluation methodology overlooks two important desiderata that speech representations should have: robustness to domain shifts and transferability to other languages. In this paper we learn representations from up to 8000 hours of diverse and noisy speech data and evaluate the representations by looking at their robustness to domain shifts and their ability to improve recognition performance in many languages. We find that our representations confer significant robustness advantages to the resulting recognition systems: we see significant improvements in out-of-domain transfer relative to baseline feature sets and the features likewise provide improvements in 25 phonetically diverse languages including tonal languages and low-resource languages.

A microstrip diplexer with very high isolation is presented. The diplexer consists of two compact filters with quarter-wavelength resonators coupled by the metallised vias. Each metallised via is inductive and behaves as a K-inverter. This type of filter has two adjustable transmission zeros on each side of the passband, which can be shifted by changing the coupling coefficient between the source and load. By locating the transmission zero of one channel at the passband of the other channel, very high isolation of the diplexer can be achieved. Simulated results of the fabricated diplexer agree well with measured results.

High isolation diplexer using quarter-wavelength resonator filter

A dual-band bandpass filter using both an open stub-loaded resonator (OSLR) and a short stub-loaded resonator (SSLR) is presented. The use of SSLR has the advantage that a transmission zero can be flexibly controlled by tuning the length of the short stub, which can be employed as a new but convenient way to create transmission zeros in front of passbands in filter design. By further introducing a two-order planar transversal filter structure using the OSLR as well as the source-load coupling, a dual-band filter is implemented with four transmission zeros and exhibits high skirt selectivity based on the simulated result. Two passbands with center frequencies of 2.45 GHz and 3.5 GHz are achieved with compact size of about 0.18λ × 0.12λ at 2.45 GHz.

Dual-band bandpass filter using stub loaded resonators with multiple transmission zeros

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advertorch v0.1: An Adversarial Robustness Toolbox based on PyTorch.

Learning Robust and Multilingual Speech Representations

Learning Robust and Multilingual Speech Representations

High isolation diplexer using quarter-wavelength resonator filter

Dual-band bandpass filter using stub loaded resonators with multiple transmission zeros