Named Entity Detection and Injection for Direct Speech Translation

In a sentence, certain words are critical for its semantic. Among them, named entities (NEs) are notoriously challenging for neural models. Despite their importance, their accurate handling has been neglected in speech-to-text (S2T) translation research, and recent work has shown that S2T models perform poorly for locations and notably person names, whose spelling is challenging unless known in advance. In this work, we explore how to leverage dictionaries of NEs known to likely appear in a given context to improve S2T model outputs. Our experiments show that we can reliably detect NEs likely present in an utterance starting from S2T encoder outputs. Indeed, we demonstrate that the current detection quality is sufficient to improve NE accuracy in the translation with a 31% reduction in person name errors.Comment: \c{opyright} 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work

A nitroxide-containing cathode material for organic radical batteries studied with pulsed EPR spectroscopy

An electron spin echo in a nitroxide-containing polymer cathode film for organic radical batteries is observed for various states of charge at cryogenic temperatures. The EPR-detected state of charge (ESOC), as inferred from the number of paramagnetic centers in the film, is compared to the results of Coulomb counting based on galvanostatic charging. Spin concentration, longitudinal relaxation times T(1 )and phase memory times T-m strongly correlate with the ESOC. In the discharged film, the spin concentration reaches 5 +/- 3x10(20) cm(-3), causing a phase memory time T-m << 100 ns (shorter than the resonator ring-down time) that hinders the detection of the spin echo. In the charged film, the decreased spin concentration results in a longer T-m between 100 ns and 300 ns that enables spin-echo detection, yet limits the length of the microwave pulse sequence. The short, broad-band pulses cause instantaneous diffusion in the unoxidized domains across the oxidized film, affecting the relative peak intensities in the pulsed EPR spectrum. By simulating the spectral distortion caused by instantaneous diffusion, we obtain information on the local spin concentration, which complements the information on the 'bulk' spin concentration determined by electrochemistry and continuous-wave EPR spectroscopy

a [Ni(Salen)]‐TEMPO redox‐conducting polymer for organic batteries

Redox-active nitroxyl-containing polymers are promising candidates as possible replacements for inorganic based energy-storage materials, due to their high energy density and fast redox kinetics. One challenge towards the implementation of such a system is the insufficient electrical conductivity, impeding the charge collection even with highly conductive additives. Herein, the first implementation of a polymeric bis(salicylideniminato) nickel (NiSalen) conductive backbone as an active charge-collecting wire is reported. NiSalen simultaneously serves as a charge collector for nitroxyl pendants and supports the redox capacity of the material. This novel polymer exhibits a specific capacity of up to 91.5 mAh g−1, retaining 87 % of its theoretical capacity at 800 C and more than 30 % at as high as 3000 C (66 % capacity retention after 2000 cycles). The properties of the new material upon operation was studied by means of operando electrochemical methods, UV-Vis, and electron paramagnetic resonance spectroscopy

Spins at work: probing charging and discharging of organic radical batteries by electron paramagnetic resonance spectroscopy

Organic radical batteries (ORBs) are a promising class of electrochemical power sources employing organic radicals as redox-active groups. This article reports on the development of a versatile on-substrate electrode setup for spectroelectrochemical Electron Paramagnetic Resonance (EPR) measurements on redox conductive polymers for ORBs. Quantitative in operando EPR experiments performed on electrochemical cells with a di-TEMPO Ni-Salen polymer as active electrode material demonstrate a strong decrease in the number of paramagnetic centers upon oxidizing the film. The distinct EPR signatures of the TEMPO-containing polymer and its fragments in different molecular environments are used to study its degradation upon repeated cycling. A comparison between the number of EPR-active sites and the number of electrochemically active charges, as measured by cyclic voltammetry, provides information on the nature of the degradation process. Low-temperature ex situ pulse EPR measurements on the oxidized polymer film reveal the spectrum of dilute nitroxide species, which may be associated with electrochemically inactive islands. These experiments pave the way for advanced EPR techniques for accurately determining distances between adjacent paramagnetic centers and thus for identifying performance-limiting loss mechanisms, which can eventually help develop strategies for making ORBs powerful contenders on the path towards sustainable electrochemical power sources

SeamlessM4T-Massively Multilingual & Multimodal Machine Translation

What does it take to create the Babel Fish, a tool that can help individuals translate speech between any two languages? While recent breakthroughs in text-based models have pushed machine translation coverage beyond 200 languages, unified speech-to-speech translation models have yet to achieve similar strides. More specifically, conventional speech-to-speech translation systems rely on cascaded systems that perform translation progressively, putting high-performing unified systems out of reach. To address these gaps, we introduce SeamlessM4T, a single model that supports speech-to-speech translation, speech-to-text translation, text-to-speech translation, text-to-text translation, and automatic speech recognition for up to 100 languages. To build this, we used 1 million hours of open speech audio data to learn self-supervised speech representations with w2v-BERT 2.0. Subsequently, we created a multimodal corpus of automatically aligned speech translations. Filtered and combined with human-labeled and pseudo-labeled data, we developed the first multilingual system capable of translating from and into English for both speech and text. On FLEURS, SeamlessM4T sets a new standard for translations into multiple target languages, achieving an improvement of 20% BLEU over the previous SOTA in direct speech-to-text translation. Compared to strong cascaded models, SeamlessM4T improves the quality of into-English translation by 1.3 BLEU points in speech-to-text and by 2.6 ASR-BLEU points in speech-to-speech. Tested for robustness, our system performs better against background noises and speaker variations in speech-to-text tasks compared to the current SOTA model. Critically, we evaluated SeamlessM4T on gender bias and added toxicity to assess translation safety. Finally, all contributions in this work are open-sourced and accessible at https://github.com/facebookresearch/seamless_communicatio