Papers with Code 아카이브
Speech Recognition
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Speech Recognition is the task of converting spoken language into text. It involves recognizing the words spoken in an audio recording and transcribing them into a written format. The goal is to accurately transcribe the speech in real-time or from recorded audio, taking into account factors such as accents, speaking speed, and background noise.
<span style="color:grey; opacity: 0.6">( Image credit: SpecAugment )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/DenseAI/awesome-medical-machine-learning
https://github.com/DevonFulcher/CryptoPricePredictor
https://github.com/DiegoLeon96/Neural-Speech-Dereverberation
https://github.com/Digital-Umuganda/Deepspeech-Kinyarwanda
https://github.com/DomhnallBoyle/sv2s
https://github.com/Duplums/bhb10k-dl-benchmark
https://github.com/Edresson/VoiceSplit
VoiceFilter: Targeted Voice Separation by Speaker-Conditioned Spectrogram Masking
https://github.com/Edresson/ZS-TTS-Evaluation
https://github.com/EigenPro/EigenPro
Evaluation of Neural Architectures Trained with Square Loss vs Cross-Entropy in Classification Tasks
https://github.com/ElenaRyumina/EMO-AffectNetModel
https://github.com/Elvictor01/Plant-Pathology-2020---FGVC7-Identify-the-category-of-foliar-diseases-in-apple-trees
https://github.com/EmanueleC/DRMM_repro
https://github.com/ErickaBermudez/har-fl-simulation
Communication-Efficient Learning of Deep Networks from Decentralized Data
https://github.com/EscVM/EscVM_YT/blob/master/Notebooks/1%20-%20TF2.X%20DeepAI-Quickie/tf_2_keyword_transformer.ipynb
Keyword Transformer: A Self-Attention Model for Keyword Spotting
https://github.com/FAKEBOB-adversarial-attack/FAKEBOB
Who is Real Bob? Adversarial Attacks on Speaker Recognition Systems
https://github.com/FastAndFourier/MLA-Project-DYSARTHRIA
https://github.com/FazedAI/OpenSeq2Seq
Mixed-Precision Training for NLP and Speech Recognition with OpenSeq2Seq
https://github.com/Fengdalu/Lipreading-DenseNet3D
LRW-1000: A Naturally-Distributed Large-Scale Benchmark for Lip Reading in the Wild
https://github.com/FraCorti/Deep_Subnetworks_for_Dynamic_Resource_Constraints
Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition
https://github.com/FredericGodin/QuasiRNN-DReLU
https://github.com/Fuann/TENET
TENET: A Time-reversal Enhancement Network for Noise-robust ASR
https://github.com/GNovich/Talk2Me
Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis
https://github.com/GPUPhobia/vocal-mask
https://github.com/GSNCodes/Breast_Cancer_Detection_CNN
https://github.com/Gal1eo/DT2119
https://github.com/Garfield35/Speach-Recognition-Using-Tensorflow
https://github.com/Garvit-32/Non-Attentive-Tacotron
https://github.com/GeoTrouvetout/Vehicle_ReID
https://github.com/George0828Zhang/simulst
https://github.com/GeorgeFedoseev/DeepSpeech
https://github.com/Golesheed/whisper-jasmin-cgn
https://github.com/Goodideax/lstm-negtive
https://github.com/Goodideax/rnn_neg_efficient
https://github.com/Guillem96/data2vec-vision
data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language
https://github.com/GustikS/NeuraLogic
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.