Results 31 to 40 of about 315,110 (278)
Frontiers in Computational Neuroscience, 2022 IntroductionIn recent years, machines powered by deep learning have achieved near-human levels of performance in speech recognition. The fields of artificial intelligence and cognitive neuroscience have finally reached a similar level of performance ...
Cai Wingfield +9 more
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Language Modeling with Highway LSTM
, 2017 Language models (LMs) based on Long Short Term Memory (LSTM) have shown good gains in many automatic speech recognition tasks. In this paper, we extend an LSTM by adding highway networks inside an LSTM and use the resulting Highway LSTM (HW-LSTM) model ...
Kurata, Gakuto +3 more
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Integrating morphology into automatic speech recognition
2009 IEEE Workshop on Automatic Speech Recognition & Understanding, 2009 This paper proposes a novel approach to integrate the morphology as a model into an automatic speech recognition (ASR) system for morphologically rich languages. The high out-of-vocabulary (OOV) word rates have been a major challenge for ASR in morphologically productive languages.
Hasim Sak +2 more
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Automatic Speech Recognition from Neural Signals: A Focused Review
Frontiers in Neuroscience, 2016 Speech interfaces have become widely accepted and are nowadays integrated in various real-life applications and devices. They have become a part of our daily life. However, speech interfaces presume the ability to produce intelligible speech, which might
Christian Herff, Tanja Schultz
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Audio-Visual Speech and Gesture Recognition by Sensors of Mobile Devices
Sensors, 2023 Audio-visual speech recognition (AVSR) is one of the most promising solutions for reliable speech recognition, particularly when audio is corrupted by noise. Additional visual information can be used for both automatic lip-reading and gesture recognition.
Dmitry Ryumin +2 more
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Speech and crosstalk detection in multichannel audio [PDF]
, 2005 The analysis of scenarios in which a number of microphones record the activity of speakers, such as in a round-table meeting, presents a number of computational challenges.
Brown, G.J. +3 more
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A Method Improves Speech Recognition with Contrastive Learning in Low-Resource Languages
Applied Sciences, 2023 Building an effective automatic speech recognition system typically requires a large amount of high-quality labeled data; However, this can be challenging for low-resource languages.
Lixu Sun, Nurmemet Yolwas, Lina Jiang
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English Broadcast News Speech Recognition by Humans and Machines
, 2019 With recent advances in deep learning, considerable attention has been given to achieving automatic speech recognition performance close to human performance on tasks like conversational telephone speech (CTS) recognition.
Dibert, Tom +10 more
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Automatic Speech Recognition for Hindi
CoRRAutomatic speech recognition (ASR) is a key area in computational linguistics, focusing on developing technologies that enable computers to convert spoken language into text. This field combines linguistics and machine learning. ASR models, which map speech audio to transcripts through supervised learning, require handling real and unrestricted text ...
Anish Saha, A. G. Ramakrishnan
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SVMs for Automatic Speech Recognition: A Survey [PDF]
, 2007 Hidden Markov Models (HMMs) are, undoubtedly, the most employed core technique for Automatic Speech Recognition (ASR). Nevertheless, we are still far from achieving high-performance ASR systems. Some alternative approaches, most of them based on Artificial Neural Networks (ANNs), were proposed during the late eighties and early nineties.
Rubén Solera-Ureña +5 more
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