162,241 research outputs found
Advances and trends in automatic speech recognition
This paper aimts at giving an overview of récent advances in the domain of
Speech Recognition . The paper mainly focttses on Speech Recognition, but
also mentions some progress in other areas of Speech Processing (spea er
recognition, speech synthesis, speech analysis and coding) using similar
methodologies.
It first gives a view of what the problems related to aulomatic speech
processing are, and then describes the initial approaches that have been
followed in order to address Chose problems .
It then introduces thé methodological novelties that allowed for progress
along three axes : from isolated-word recognition to continuous speech,
from spea er-dependent recognition to spea er-independent, and from
small vocabularies to large rocabularies. Special emphasis centers on tlie improvements made possible by Mar ov Models . and, more recently, hy
Connectionist Models, resulting in progress simultaneously obtained along
the above différent axes, in improved performance for difficult vocabularies,
or in more robust systems . Some specialised hardware is also
described, as well as the efforts aimed ai assessing Speech Recognition
systems.Le but de cet article est de donner un aperçu des progrès récents obtenus
dans le domaine de la reconnaissance automatique de la parole . Il traite
essentiellement de la reconnaissance vocale, mais mentionne Ă©galement
les progrès réalisés dans d'autres domaines du Traitement Automatique
de la Parole (Reconnaissance du Locuteur, Synthèse de Parole . Analyse
et Codage), qui utilisent des méthodes voisines.
Ensuite, sont introduites les nouveautés méthodologiques qui ont permis
des progrès suivant trois axes : des mots isolés vers la parole continue, de
la reconnaissance monolocuteur vers la reconnaissance multilocuteur, et
des petits vocabulaires vers les grands vocabulaires . Une mention
spéciale est accordée aux améliorations qui ont été rendues possibles par
les Modèles Mar oviens, et, plus récemment, par les Modèles Connexionnistes . Ces méthodes ont conduit à des progrès obtenus
concurremment suivant plusieurs axes, Ă des performances meilleures sur
les vocabulaires difficiles, ou à des systèmes plus robustes . Quelques
matériels spécialisés sont également décrits, ainsi que les efforts qui ont
été consentis dans le but d'évaluer la qualité des systèmes de reconnaissanc
Advances in optimisation algorithms and techniques for deep learning
In the last decade, deep learning(DL) has witnessed excellent performances on a variety of problems, including speech recognition, object recognition, detection, and natural language processing (NLP) among many others. Of these applications, one common challenge is to obtain ideal parameters during the training of the deep neural networks (DNN). These typical parameters are obtained by some optimisation techniques which have been studied extensively. These research have produced state-of-art(SOTA) results on speed and memory improvements for deep neural networks(NN) architectures. However, the SOTA optimisers have continued to be an active research area with no compilations of the existing optimisers reported in the literature. This paper provides an overview of the recent advances in optimisation algorithms and techniques used in DNN, highlighting the current SOTA optimisers, improvements made on these optimisation algorithms and techniques, alongside the trends in the development of optimisers used in training DL based models. The results of the search of the Scopus database for the optimisers in DL provides the articles reported as the summary of the DL optimisers. From what we can tell, there is no comprehensive compilation of the optimisation algorithms and techniques so far developed and used in DL research and applications, and this paper summarises these facts
Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives
Over the past few years, adversarial training has become an extremely active
research topic and has been successfully applied to various Artificial
Intelligence (AI) domains. As a potentially crucial technique for the
development of the next generation of emotional AI systems, we herein provide a
comprehensive overview of the application of adversarial training to affective
computing and sentiment analysis. Various representative adversarial training
algorithms are explained and discussed accordingly, aimed at tackling diverse
challenges associated with emotional AI systems. Further, we highlight a range
of potential future research directions. We expect that this overview will help
facilitate the development of adversarial training for affective computing and
sentiment analysis in both the academic and industrial communities
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