https://github.com/park-cheol/ASR-Conformer
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
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/park-cheol/ASR-Transformer
https://github.com/parkervg/allrecipes-bert
VideoBERT: A Joint Model for Video and Language Representation Learning
https://github.com/pascalbakker/WaveNet-Implementation
https://github.com/pathmmu-benchmark/pathmmu
https://github.com/pawan47/nilmtk_readings
Neural NILM: Deep Neural Networks Applied to Energy Disaggregation
https://github.com/payalmohapatra/Multimodal-Speech-Disfluency
https://github.com/pbrandl/aNN_Audio
https://github.com/pedrodke/libris2s
LibriVoxDeEn: A Corpus for German-to-English Speech Translation and German Speech Recognition
https://github.com/peta78/linear-regression-voting-and-statistics
https://github.com/petuum/adaptdl
AdaScale SGD: A User-Friendly Algorithm for Distributed Training
https://github.com/peustr/wavenet
https://github.com/phanxuanphucnd/Arizona-asr
Conformer: Convolution-augmented Transformer for Speech Recognition
https://github.com/phanxuanphucnd/Arizona-spotting
wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations
https://github.com/phanxuanphucnd/conformer
Conformer: Convolution-augmented Transformer for Speech Recognition
https://github.com/phanxuanphucnd/wav2asr
wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations
https://github.com/phanxuanphucnd/wav2kws
Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition
https://github.com/pheepa/DCUnet
https://github.com/phellonchen/x-llm
https://github.com/philsyn/diffwave-unconditional
https://github.com/philsyn/diffwave-vocoder
https://github.com/pirxus/personalVAD
https://github.com/plachtaa/vall-e-x
Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers
https://github.com/playvoice/grad-svc
Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech
https://github.com/pmcgrath249/DMMFODS
https://github.com/pmcgrath249/DeepCVLab
https://github.com/pmcgrath249/DeepCV_Lab
https://github.com/pragyak412/Improving-Voice-Separation-by-Incorporating-End-To-End-Speech-Recognition
Improving Voice Separation by Incorporating End-to-end Speech Recognition
https://github.com/pranaymanocha/PerceptualAudio
https://github.com/prerak23/dir_srcmic_doa
The DIRHA-English corpus and related tasks for distant-speech recognition in domestic environments
https://github.com/prijatelj/crowd_noise
https://github.com/priyankadeybhadra/CNN-on-CIFR
https://github.com/priyavrat-misra/xrays-and-gradcam
https://github.com/pspratling/Speech-Recognition
Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition
https://github.com/pswietojanski/ojsp_adaptation_review_2020
Adaptation Algorithms for Neural Network-Based Speech Recognition: An Overview
https://github.com/pudae/tensorflow-densenet
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.