Results 31 to 40 of about 3,274,646 (190)
A study of transformer-based end-to-end speech recognition system for Kazakh language
Scientific Reports, 2022 Today, the Transformer model, which allows parallelization and also has its own internal attention, has been widely used in the field of speech recognition.
Mamyrbayev Orken +4 more
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A study on constraining Connectionist Temporal Classification for temporal audio alignment
Interspeech 2022, 2022 Connectionist Temporal Classification (CTC) has become a standard for deep learning-based temporal alignment allowing relevant probabilistic distributions to be learned. However, by nature, CTC is a transcription objective that can be minimized without guaranteeing any alignment properties.
Teytaut, Yann +2 more
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Modeling Intra-label Dynamics and Analyzing the Role of Blank in Connectionist Temporal Classification [PDF]
Computer and Knowledge Engineering, 2018 The goal of many tasks in the realm of sequence processing is to map a sequence of input data to a sequence of output labels. Long short-term memory (LSTM), a type of recurrent neural network (RNN), equipped with connectionist temporal classification ...
Ashkan Sadeghi Lotfabadi +2 more
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Fast offline transformer-based end-to-end automatic speech recognition for real-world applications
ETRI Journal, 2022 With the recent advances in technology, automatic speech recognition (ASR) has been widely used in real-world applications. The efficiency of converting large amounts of speech into text accurately with limited resources has become more vital than ever ...
Yoo Rhee Oh, Kiyoung Park, Kiyoung Park
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Sensors, 2020 Advanced automatic pronunciation error detection (APED) algorithms are usually based on state-of-the-art automatic speech recognition (ASR) techniques. With the development of deep learning technology, end-to-end ASR technology has gradually matured and ...
Long Zhang +7 more
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Recognition of English speech – using a deep learning algorithm
Journal of Intelligent Systems, 2023 The accurate recognition of speech is beneficial to the fields of machine translation and intelligent human–computer interaction. After briefly introducing speech recognition algorithms, this study proposed to recognize speech with a recurrent neural ...
Wang Shuyan
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IEEE Access, 2021 Offline handwritten Chinese text recognition is one of the most challenging tasks in that it involves various writing styles, complex character-touching, and large number of character categories.
Yintong Wang +3 more
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Self-attention Networks for Connectionist Temporal Classification in Speech Recognition [PDF]
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019 The success of self-attention in NLP has led to recent applications in end-to-end encoder-decoder architectures for speech recognition. Separately, connectionist temporal classification (CTC) has matured as an alignment-free, non-autoregressive approach to sequence transduction, either by itself or in various multitask and decoding frameworks.
Julian Salazar +2 more
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American Sign Language Alphabet Recognition Using Inertial Motion Capture System with Deep Learning
Inventions, 2022 Sign language is designed as a natural communication method for the deaf community to convey messages and connect with society. In American sign language, twenty-six special sign gestures from the alphabet are used for the fingerspelling of proper words.
Yutong Gu +5 more
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Nasal Speech Sounds Detection Using Connectionist Temporal Classification [PDF]
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018 Phone attributes, known also as distinctive or phonological features, belong to important classification of the speech sounds used in automatic speech processing. Training of conventional phone attribute detectors (classifiers), either based on acoustic measurements or deep learning approaches, requires decent phone boundary segmentation.
Cernak, Milos, Tong, Sibo
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