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Drivers of Precision Agriculture Adoption in Italian Viticulture
Applied Economic Perspectives and Policy, EarlyView.ABSTRACT This study examines the main drivers influencing the adoption of two types of precision farming technologies in the viticultural sector: Decision Support Systems (DSSs) and Variable Rate Technologies (VRTs). We apply a partial proportional odds model and find that socio‐demographic characteristics are not significant determinants of adoption ...
Olimpia Fontana +3 more
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The use of speech knowledge in automatic speech recognition
Proceedings of the IEEE, 1985In automatic speech recognition, the acoustic signal is the only tangible connection between the talker and the machine. While the signal conveys linguistic information, this information is often encoded in such a complex manner that the signal exhibits a great deal of variability.
V W Zué
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Automatic speech recognition: a survey
Multimedia Tools and Applications, 2020Recently great strides have been made in the field of automatic speech recognition (ASR) by using various deep learning techniques. In this study, we present a thorough comparison between cutting-edged techniques currently being used in this area, with a special focus on the various deep learning methods.
Mishaim Malik +3 more
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Turbo Automatic Speech Recognition
IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2016Performance of automatic speech recognition (ASR) systems can significantly be improved by integrating further sources of information such as additional modalities, or acoustic channels, or acoustic models. Given the arising problem of information fusion, striking parallels to problems in digital communications are exhibited, where the discovery of the
Simon Receveur +2 more
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Active learning for automatic speech recognition
IIEEE International Conference on Acoustics Speech and Signal Processing, 2002State-of-the-art speech recognition systems are trained using transcribed utterances, preparation of which is labor intensive and time-consuming. In this paper, we describe a new method for reducing the transcription effort for training in automatic speech recognition (ASR). Active learning aims at reducing the number of training examples to be labeled
Riccardi, Giuseppe, D. Tur, A. Gorin
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Automatic Speech Recognition and Intrinsic Speech Variation
2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings, 2006This paper briefly reviews state of the art related to the topic of speech variability sources in automatic speech recognition systems. It focuses on some variations within the speech signal that make the ASR task difficult. The variations detailed in the paper are intrinsic to the speech and affect the different levels of the ASR processing chain. For
BENZEGUIBA M. +12 more
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The Journal of the Acoustical Society of America, 2000
Speech recognition is carried out by matching parameterized speech with a dynamically extended network of paths comprising model linguistic elements (12b, 12c). The units are context related, e.g. triphones. Some elements cannot be converted to models at the time when it is necessary to incorporate the element into the paths because the context is not ...
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Speech recognition is carried out by matching parameterized speech with a dynamically extended network of paths comprising model linguistic elements (12b, 12c). The units are context related, e.g. triphones. Some elements cannot be converted to models at the time when it is necessary to incorporate the element into the paths because the context is not ...
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2015 CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON), 2015
This Plenary presents automatic speech recognition (ASR) as a task of artificial intelligence. The basis, the methodology, spectral processing, distance measures for speech, segmentation speech, spectral and temporal variability, application of Markov Models, noise robustness, Language Models for ASR, are presented.
Dong Yu, Li Deng
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This Plenary presents automatic speech recognition (ASR) as a task of artificial intelligence. The basis, the methodology, spectral processing, distance measures for speech, segmentation speech, spectral and temporal variability, application of Markov Models, noise robustness, Language Models for ASR, are presented.
Dong Yu, Li Deng
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The Journal of the Acoustical Society of America, 2005
Great strides have been made in the development of automatic speech recognition (ASR) technology over the past thirty years. Most of this effort has been centered around the extension and improvement of Hidden Markov Model (HMM) approaches to ASR. Current commercially-available and industry systems based on HMMs can perform well for certain situational
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Great strides have been made in the development of automatic speech recognition (ASR) technology over the past thirty years. Most of this effort has been centered around the extension and improvement of Hidden Markov Model (HMM) approaches to ASR. Current commercially-available and industry systems based on HMMs can perform well for certain situational
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2021
This chapter is entirely dedicated to automatic speech recognition (ASR) which is one of the most complex fields of machine learning. Topics from signal processing and the properties of the acoustic signal to acoustic and language modeling, pronunciation modeling and performance analysis will all be explained in an easily comprehensible manner.
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This chapter is entirely dedicated to automatic speech recognition (ASR) which is one of the most complex fields of machine learning. Topics from signal processing and the properties of the acoustic signal to acoustic and language modeling, pronunciation modeling and performance analysis will all be explained in an easily comprehensible manner.
openaire +1 more source