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Speech Recognition with No Speech or with Noisy Speech [PDF]
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019 The performance of automatic speech recognition systems(ASR) degrades in the presence of noisy speech. This paper demonstrates that using electroencephalography (EEG) can help automatic speech recognition systems overcome performance loss in the presence of noise.
Gautam Krishna +3 more
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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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, 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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WIREs Cognitive Science, 2012 AbstractClassic research on the perception of speech sought to identify minimal acoustic correlates of each consonant and vowel. In explaining perception, this view designated momentary components of an acoustic spectrum as cues to the recognition of elementary phonemes.
Remez, Robert E, Thomas, Emily F
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Band importance for speech-in-speech recognition [PDF]
JASA Express Letters, 2021 Predicting masked speech perception typically relies on estimates of the spectral distribution of cues supporting recognition. Current methods for estimating band importance for speech-in-noise use filtered stimuli. These methods are not appropriate for speech-in-speech because filtering can modify stimulus features affecting auditory stream ...
Buss, Emily, Bosen, Adam
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Speech recognition in parallel [PDF]
Proceedings of the workshop on Speech and Natural Language - HLT '89, 1989 Concomitantly with recent advances in speech coding, recognition and production, parallel computer systems are now commonplace delivering raw computing power measured in hundreds of MIPS and Megaflops. It seems inevitable that within the next decade or so, gigaflop parallel processors will be achievable at modest cost.
Salvatore J. Stolfo +3 more
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Unsupervised Speech Recognition
CoRR, 2021 Despite rapid progress in the recent past, current speech recognition systems still require labeled training data which limits this technology to a small fraction of the languages spoken around the globe. This paper describes wav2vec-U, short for wav2vec Unsupervised, a method to train speech recognition models without any labeled data.
Alexei Baevski +3 more
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Continuous speech recognition [PDF]
IEEE Signal Processing Magazine, 1995 The authors focus on a tutorial description of the hybrid HMM/ANN method. The approach has been applied to large vocabulary continuous speech recognition, and variants are in use by many researchers, The method provides a mechanism for incorporating a range of sources of evidence without strong assumptions about their joint statistics, and may have ...
Nelson Morgan, Hervé Bourlard
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Speech Recognition with Augmented Synthesized Speech [PDF]
2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), 2019 Recent success of the Tacotron speech synthesis architecture and its variants in producing natural sounding multi-speaker synthesized speech has raised the exciting possibility of replacing expensive, manually transcribed, domain-specific, human speech that is used to train speech recognizers.
Andrew Rosenberg +6 more
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