Results 11 to 20 of about 97,475 (267)
Gazi Üniversitesi Fen Bilimleri Dergisi, 2019 Speech is a paramount means of communication among humans, which makes recognition of the speech by computers is a study area of significance. In this research area, many studies have been carried out based on different languages.
Saadin OYUCU +2 more
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Trends in Hearing, 2020 Several studies have demonstrated that extended high frequencies (EHFs; >8 kHz) in speech are not only audible but also have some utility for speech recognition, including for speech-in-speech recognition when maskers are facing away from the listener ...
Allison Trine, Brian B. Monson
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Research Status and Prospect of Transformer in Speech Recognition
Jisuanji kexue yu tansuo, 2021 As a new deep learning algorithm framework, Transformer has attracted more and more researchers?? attention and has become a current research hotspot. Inspired by humans focusing on important things only, the self-attention mechanism in the Transformer ...
ZHANG Xiaoxu, MA Zhiqiang, LIU Zhiqiang, ZHU Fangyuan, WANG Chunyu
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Learning speech rate in speech recognition [PDF]
Interspeech 2015, 2015 A significant performance reduction is often observed in speech recognition when the rate of speech (ROS) is too low or too high. Most of present approaches to addressing the ROS variation focus on the change of speech signals in dynamic properties caused by ROS, and accordingly modify the dynamic model, e.g., the transition probabilities of the hidden
Xiangyu Zeng, Shi Yin, Dong Wang 0013
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EURASIP Journal on Audio, Speech, and Music Processing, 2021 The performance of speech recognition systems trained with neutral utterances degrades significantly when these systems are tested with emotional speech. Since everybody can speak emotionally in the real-world environment, it is necessary to take account
Masoud Geravanchizadeh +2 more
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Non-autoregressive Transformer Chinese Speech Recognition Incorporating Pronunciation- Character Representation Conversion [PDF]
Jisuanji kexue, 2023 The Transformer based on self-attention mechanism shows powerful model performance in speech recognition tasks,where the non-autoregressive Transformer automatic speech recognition model has a faster decoding speed compared with the autoregressive model ...
TENG Sihang, WANG Lie, LI Ya
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Selection of acoustic modeling unit for Tibetan speech recognition based on deep learning [PDF]
MATEC Web of Conferences, 2021 The selection of the speech recognition modeling unit is the primary problem of acoustic modeling in speech recognition, and different acoustic modeling units will directly affect the overall performance of speech recognition.
Gong Baojia +4 more
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, 2021 AbstractSpeech recognition can be formulated as the problem of guessing a sequence of words that produces a sequence of sounds. The human brain is remarkably good at solving this problem, even though the same words correspond to many different sounds, because of accents or characteristics of the voice. Moreover, the environment is always noisy, to that
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Advancing Speech Recognition With No Speech Or With Noisy Speech [PDF]
2019 27th European Signal Processing Conference (EUSIPCO), 2019 In this paper we demonstrate end-to-end continuous speech recognition (CSR) using electroencephalography (EEG) signals with no speech signal as input. An attention model based automatic speech recognition (ASR) and connectionist temporal classification (CTC) based ASR systems were implemented for performing recognition.
Gautam Krishna +3 more
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A novel privacy-preserving speech recognition framework using bidirectional LSTM
Journal of Cloud Computing: Advances, Systems and Applications, 2020 Utilizing speech as the transmission medium in Internet of things (IoTs) is an effective way to reduce latency while improving the efficiency of human-machine interaction. In the field of speech recognition, Recurrent Neural Network (RNN) has significant
Qingren Wang +4 more
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