2,218 research outputs found
Multidisciplinary perspectives on Artificial Intelligence and the law
This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio
RADIC Voice Authentication: Replay Attack Detection using Image Classification for Voice Authentication Systems
Systems like Google Home, Alexa, and Siri that use voice-based authentication to verify their users’ identities are vulnerable to voice replay attacks. These attacks gain unauthorized access to voice-controlled devices or systems by replaying recordings of passphrases and voice commands. This shows the necessity to develop more resilient voice-based authentication systems that can detect voice replay attacks.
This thesis implements a system that detects voice-based replay attacks by using deep learning and image classification of voice spectrograms to differentiate between live and recorded speech. Tests of this system indicate that the approach represents a promising direction for detecting voice-based replay attacks
Universal Automatic Phonetic Transcription into the International Phonetic Alphabet
This paper presents a state-of-the-art model for transcribing speech in any
language into the International Phonetic Alphabet (IPA). Transcription of
spoken languages into IPA is an essential yet time-consuming process in
language documentation, and even partially automating this process has the
potential to drastically speed up the documentation of endangered languages.
Like the previous best speech-to-IPA model (Wav2Vec2Phoneme), our model is
based on wav2vec 2.0 and is fine-tuned to predict IPA from audio input. We use
training data from seven languages from CommonVoice 11.0, transcribed into IPA
semi-automatically. Although this training dataset is much smaller than
Wav2Vec2Phoneme's, its higher quality lets our model achieve comparable or
better results. Furthermore, we show that the quality of our universal
speech-to-IPA models is close to that of human annotators.Comment: 5 pages, 7 table
Voicebox: Text-Guided Multilingual Universal Speech Generation at Scale
Large-scale generative models such as GPT and DALL-E have revolutionized
natural language processing and computer vision research. These models not only
generate high fidelity text or image outputs, but are also generalists which
can solve tasks not explicitly taught. In contrast, speech generative models
are still primitive in terms of scale and task generalization. In this paper,
we present Voicebox, the most versatile text-guided generative model for speech
at scale. Voicebox is a non-autoregressive flow-matching model trained to
infill speech, given audio context and text, trained on over 50K hours of
speech that are neither filtered nor enhanced. Similar to GPT, Voicebox can
perform many different tasks through in-context learning, but is more flexible
as it can also condition on future context. Voicebox can be used for mono or
cross-lingual zero-shot text-to-speech synthesis, noise removal, content
editing, style conversion, and diverse sample generation. In particular,
Voicebox outperforms the state-of-the-art zero-shot TTS model VALL-E on both
intelligibility (5.9% vs 1.9% word error rates) and audio similarity (0.580 vs
0.681) while being up to 20 times faster. See voicebox.metademolab.com for a
demo of the model
2023-2024 Catalog
The 2023-2024 Governors State University Undergraduate and Graduate Catalog is a comprehensive listing of current information regarding:Degree RequirementsCourse OfferingsUndergraduate and Graduate Rules and Regulation
Algorithms for light applications: from theoretical simulations to prototyping
[eng] Although the first LED dates to the middle of the 20th century, it has not been until the last decade that the market has been flooded with high efficiency and high durability LED solutions compared to previous technologies. In addition, luminaires that include types of LEDs differentiated in hue or color have already appeared. These luminaires offer new possibilities to reach colorimetric or non-visual capabilities not seen to date.
Due to the enormous number of LEDs on the market, with very different spectral characteristics, the use of the spectrometer as a measuring device for determining LEDs properties has become popular. Obtaining colorimetric information from a luminaire is a necessary step to commercialize it, so it is a tool commonly used by many LED manufacturers.
This doctoral thesis advances the state-of-the-art and knowledge of LED technology at the level of combined spectral emission, as well as applying innovative spectral reconstruction techniques to a commercial multichannel colorimetric sensor. On the one hand, new spectral simulation algorithms that allow obtaining a very high number of results have been developed, being able to obtain optimized values of colorimetric and non-visual parameters in multichannel light sources. MareNostrum supercomputer has been used and new relationships between colorimetric and non-visual parameters in commercial white LED datasets have been found through data analysis. Moreover, the functional improvement of a multichannel colorimetric sensor has been explored by providing it with a neural network for spectral reconstruction. A large amount of data has been generated, which has allowed simulations and statistical studies on the error committed in the spectral reconstruction process using different techniques. This improvement has led to an increase in the spectral resolution measured by the sensor, allowing better accuracy in the calculation of colorimetric parameters. Prototypes of the light sources and the colorimetric sensor have been developed in order to experimentally demonstrate the theoretical framework generated. All the prototypes have been characterized and the errors generated with respect to the theoretical models have been evaluated. The results obtained have been validated through the application of different industry standards by comparison with calibrated commercial devices.[cat] Aquesta tesi doctoral realitza un avançament en l’estat de l’art i en el coneixement sobre la tecnologia LED a nivell d’emissió espectral combinada, a més d’aplicar tècniques innovadores de reconstrucció espectral a un sensor colorimètric multicanal comercial. Per una banda, s’han desenvolupat nous algoritmes de simulació espectral que permeten obtenir un nombre molt elevat de resultats, sent capaços d’obtenir valors optimitzats de parà metres colorimètrics i no-visuals en fonts de llum multicanal. S’ha fet ús del supercomputador MareNostrum i s’han trobat noves relacions entre parà metres colorimètrics i no visuals en conjunts de LEDs blancs comercials a través de l’anà lisi de dades. Per altra banda, s’ha explorat la millora funcional d’un sensor colorimètric multicanal, dotant-lo d’una xarxa neuronal per a la reconstrucció espectral. S’han generat una gran quantitat de dades que han permès realitzar simulacions i estudis estadÃstics sobre l’error comès en el procés de reconstrucció espectral utilitzant diferents tècniques. Aquesta millora ha implicat un augment de la resolució espectral mesurada pel sensor, permetent obtenir una millor precisió en el cà lcul de parà metres colorimètrics. S’han desenvolupat prototips de les fonts de llum i del sensor colorimètric amb l’objectiu de demostrar experimentalment el marc teòric generat. Tots els prototips han estat caracteritzats i s’han avaluat els errors generats respecte els models teòrics. Els resultats obtinguts s’han validat a través de l’aplicació de diferents està ndards de la indústria o a través de la comparativa amb dispositius comercials calibrats
- …