Results 41 to 50 of about 35,015 (307)
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 +9 more
doaj +1 more source
Attention-Based End-To-End Named Entity Recognition From Speech
, 2021 | openaire: EC/H2020/780069/EU//MeMADNamed entities are heavily used in the field of spoken language understanding, which uses speech as an input. The standard way of doing named entity recognition from speech involves a pipeline of two systems, where ...
Mikko Kurimo +5 more
core +1 more source
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 +2 more
doaj +1 more source
Automatic Speech Recognition for Hindi
CoRRAutomatic speech recognition (ASR) is a key area in computational linguistics, focusing on developing technologies that enable computers to convert spoken language into text. This field combines linguistics and machine learning. ASR models, which map speech audio to transcripts through supervised learning, require handling real and unrestricted text ...
Anish Saha, A. G. Ramakrishnan
openaire +2 more sources
Automatic Phonetic Transcription of Non-Prompted Speech [PDF]
, 1999 A reliable method for automatic phonetic transcription of non− prompted German speech has been developed at ...
Schiel, Florian +2 more
core +1 more source
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 +2 more
doaj +1 more source
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
doaj +1 more source
Interrogating the immune landscape of microsatellite stable RAS‐mutated colon cancer
Molecular Oncology, EarlyView.COLOSSUS project RAS‐mutated MSS colon cancer study explored transcriptomics and immune cell density by immunohistochemistry (IHC), Immunoscore (IS), ISIC/TuLIS scores, mutation counts, and detected different prevalences but similar microenvironment composition across immune markers with clinical relevance for future immunotherapy combination ...
Rodrigo Dienstmann +61 more
wiley +1 more source
KsponSpeech: Korean Spontaneous Speech Corpus for Automatic Speech Recognition
Applied Sciences, 2020 This paper introduces a large-scale spontaneous speech corpus of Korean, named KsponSpeech. This corpus contains 969 h of general open-domain dialog utterances, spoken by about 2000 native Korean speakers in a clean environment. All data were constructed
Jeong-Uk Bang +9 more
doaj +1 more source
Interpreting the effects of DNA polymerase variants at the structural level
Molecular Oncology, EarlyView.Using MAVISp and molecular dynamics simulations, we analyzed over 60 000 missense variants in POLE and POLD1 from ClinVar, COSMIC, cBioPortal, and saturation mutagenesis. Identified mechanistic indicators, including stability, binding, and long‐range, enable structural interpretation, providing ACMG‐like evidence for possible reclassification of VUS ...
Matteo Arnaudi +7 more
wiley +1 more source