Attention-enhanced connectionist temporal classification for discrete speech emotion recognition [PDF]
, 2019 Discrete speech emotion recognition (SER), the assignment of a single emotion label to an entire speech utterance, is typically performed as a sequence-to-label task. This approach, however, is limited, in that it can result in models that do not capture
Zhang, Zixing +11 more
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A Hardware-Oriented and Memory-Efficient Method for CTC Decoding
IEEE Access, 2019 The Connectionist Temporal Classification (CTC) has achieved great success in sequence to sequence analysis tasks such as automatic speech recognition (ASR) and scene text recognition (STR).
Siyuan Lu +3 more
doaj +1 more source
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
doaj +1 more source
A Deep Diacritics-Based Recognition Model for Arabic Speech: Quranic Verses as Case Study
IEEE Access, 2023 Arabic is the language of more than 422 million of the world’s population. Although classic Arabic is the Quran language that 1.9 billion Muslims are required to recite, limited Arabic speech recognition exists.
Sarah S. Alrumiah, Amal A. Al-Shargabi
doaj +1 more source
Word Beam Search: A Connectionist Temporal Classification Decoding Algorithm [PDF]
, 2018 Recurrent Neural Networks (RNNs) are used for sequence recognition tasks such as Handwritten Text Recognition (HTR) or speech recognition. If trained with the Connectionist Temporal Classification (CTC) loss function, the output of such a RNN is a matrix
Harald Scheidl +5 more
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Sound Event Detection with Sequentially Labelled Data Based on Connectionist Temporal Classification and Unsupervised Clustering [PDF]
, 2019 Sound event detection (SED) methods typically rely on either strongly labelled data or weakly labelled data. As an alternative, sequentially labelled data (SLD) was proposed.
Li, Shengchen +7 more
core +4 more sources
Speech GAU: A Single Head Attention for Mandarin Speech Recognition for Air Traffic Control
Aerospace, 2022 The rise of end-to-end (E2E) speech recognition technology in recent years has overturned the design pattern of cascading multiple subtasks in classical speech recognition and achieved direct mapping of speech input signals to text labels. In this study,
Shiyu Zhang +4 more
doaj +1 more source
Self-distillation Regularized Connectionist Temporal Classification Loss for Text Recognition: A Simple Yet Effective Approach [PDF]
, 2023 Text recognition methods are gaining rapid development. Some advanced techniques, e.g., powerful modules, language models, and un- and semi-supervised learning schemes, consecutively push the performance on public benchmarks forward. However, the problem
Li, Cheng +6 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
doaj +1 more source
Improving CTC-AED model with integrated-CTC and auxiliary loss regularization [PDF]
, 2023 Connectionist temporal classification (CTC) and attention-based encoder decoder (AED) joint training has been widely applied in automatic speech recognition (ASR).
Su, Xiangdong +2 more
core +1 more source