Results 31 to 40 of about 47,965 (148)
Real-Time End-to-End Speech Emotion Recognition with Cross-Domain Adaptation
Big Data and Cognitive Computing, 2022 Language resources are the main factor in speech-emotion-recognition (SER)-based deep learning models. Thai is a low-resource language that has a smaller data size than high-resource languages such as German. This paper describes the framework of using a
Konlakorn Wongpatikaseree +3 more
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End-To-End Silent Speech Recognition with Acoustic Sensing [PDF]
2021 IEEE Spoken Language Technology Workshop (SLT), 2021 Silent speech interfaces (SSI) has been an exciting area of recent interest. In this paper, we present a non-invasive silent speech interface that uses inaudible acoustic signals to capture people's lip movements when they speak. We exploit the speaker and microphone of the smartphone to emit signals and listen to their reflections, respectively.
Jian Luo 0007 +4 more
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End-to-End-Based Tibetan Multitask Speech Recognition
IEEE Access, 2019 To date, speech recognition technology for majority languages has been applied in wireless communication devices successfully. However, as a minority language, Tibetan has very limited resources for conventional automatic speech recognition.
Yue Zhao +4 more
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An Overview of End-to-End Automatic Speech Recognition [PDF]
Symmetry, 2019 Automatic speech recognition, especially large vocabulary continuous speech recognition, is an important issue in the field of machine learning. For a long time, the hidden Markov model (HMM)-Gaussian mixed model (GMM) has been the mainstream speech recognition framework.
Dong Wang 0054 +2 more
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MKD: Mixup-Based Knowledge Distillation for Mandarin End-to-End Speech Recognition
Algorithms, 2022 Large-scale automatic speech recognition model has achieved impressive performance. However, huge computational resources and massive amount of data are required to train an ASR model.
Xing Wu +4 more
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Performance Monitoring for End-to-End Speech Recognition [PDF]
Interspeech 2019, 2019 Submitted to Interspeech ...
Ruizhi Li, Gregory Sell, Hynek Hermansky
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End-to-End Amdo-Tibetan Speech Recognition Based on Knowledge Transfer
IEEE Access, 2020 The end-to-end speech recognition technology solves the problem that each component is independent and models cannot be jointly optimized in the traditional speech recognition model.
Xiaojun Zhu, Heming Huang
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LWMD: A Comprehensive Compression Platform for End-to-End Automatic Speech Recognition Models
Applied Sciences, 2023 Recently end-to-end (E2E) automatic speech recognition (ASR) models have achieved promising performance. However, existing models tend to adopt increasing model sizes and suffer from expensive resource consumption for real-world applications. To compress
Yukun Liu +3 more
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Variable Scale Pruning for Transformer Model Compression in End-to-End Speech Recognition
Algorithms, 2023 Transformer models are being increasingly used in end-to-end speech recognition systems for their performance. However, their substantial size poses challenges for deploying them in real-world applications.
Leila Ben Letaifa, Jean-Luc Rouas
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Applied Sciences, 2021 A Recurrent Neural Networks (RNN) based attention model has been used in code-switching speech recognition (CSSR). However, due to the sequential computation constraint of RNN, there are stronger short-range dependencies and weaker long-range ...
Zheying Huang +5 more
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