Results 21 to 30 of about 20,181 (116)

Reconsidering Read and Spontaneous Speech: Causal Perspectives on the Generation of Training Data for Automatic Speech Recognition

Information, 2023
Superficially, read and spontaneous speech—the two main kinds of training data for automatic speech recognition—appear as complementary, but are equal: pairs of texts and acoustic signals. Yet, spontaneous speech is typically harder for recognition. This
Philipp Gabler, Bernhard C. Geiger, Barbara Schuppler, Roman Kern   +3 more
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On the similarities of representations in artificial and brain neural networks for speech recognition

Frontiers in Computational Neuroscience, 2022
IntroductionIn recent years, machines powered by deep learning have achieved near-human levels of performance in speech recognition. The fields of artificial intelligence and cognitive neuroscience have finally reached a similar level of performance ...
Cai Wingfield, Chao Zhang, Barry Devereux, Elisabeth Fonteneau, Andrew Thwaites, Xunying Liu, Phil Woodland, William Marslen-Wilson, Li Su, Li Su   +9 more
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Audio-Visual Speech and Gesture Recognition by Sensors of Mobile Devices

Sensors, 2023
Audio-visual speech recognition (AVSR) is one of the most promising solutions for reliable speech recognition, particularly when audio is corrupted by noise. Additional visual information can be used for both automatic lip-reading and gesture recognition.
Dmitry Ryumin, Denis Ivanko, Elena Ryumina   +2 more
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A Method Improves Speech Recognition with Contrastive Learning in Low-Resource Languages

Applied Sciences, 2023
Building an effective automatic speech recognition system typically requires a large amount of high-quality labeled data; However, this can be challenging for low-resource languages.
Lixu Sun, Nurmemet Yolwas, Lina Jiang
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Likelihood-Maximizing-Based Multiband Spectral Subtraction for Robust Speech Recognition

EURASIP Journal on Advances in Signal Processing, 2009
Automatic speech recognition performance degrades significantly when speech is affected by environmental noise. Nowadays, the major challenge is to achieve good robustness in adverse noisy conditions so that automatic speech recognizers can be used in ...
Bagher BabaAli, Hossein Sameti, Mehran Safayani   +2 more
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Comparison of Cepstral Normalization Techniques in Whispered Speech Recognition

Advances in Electrical and Computer Engineering, 2017
This article presents an analysis of different cepstral normalization techniques in automatic recognition of whispered and bimodal speech (speech+whisper).
GROZDIC, D., JOVICIC, S., SUMARAC PAVLOVIC, D., GALIC, J., MARKOVIC, B.   +4 more
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Robust emotional speech recognition based on binaural model and emotional auditory mask in noisy environments

EURASIP Journal on Audio, Speech, and Music Processing, 2018
The performance of automatic speech recognition systems degrades in the presence of emotional states and in adverse environments (e.g., noisy conditions).
Meysam Bashirpour, Masoud Geravanchizadeh   +1 more
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Exploring the Effect of Differences in the Acoustic Correlates of Adults' and Children's Speech in the Context of Automatic Speech Recognition

EURASIP Journal on Audio, Speech, and Music Processing, 2010
This work explores the effect of mismatches between adults' and children's speech due to differences in various acoustic correlates on the automatic speech recognition performance under mismatched conditions.
Rohit Sinha, Shweta Ghai
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Speaker-dependent laser Doppler vibrometer–based voice conversion for dysarthric speech under noisy conditions [PDF]

JASA Express Letters
This study proposed the integration of a laser Doppler vibrometer sensing with a Variational Inference with adversarial learning for Text-to-Speech–based voice conversion system to enhance automatic speech recognition for individuals with dysarthria in ...
Yu-Chuan Lee, Wei-Zhong Zheng, Jia-Wei Chen, Yi-Hao Jiang, Yen-Fu Cheng, Ying-Hui Lai   +5 more
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Automatic Speech Recognition: A Comprehensive Survey

SEEU Review, 2020
Speech recognition is an interdisciplinary subfield of natural language processing (NLP) that facilitates the recognition and translation of spoken language into text by machine. Speech recognition plays an important role in digital transformation. It is
Rista Amarildo, Kadriu Arbana
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