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/sooftware/nlp-attentions
https://github.com/sooftware/sooftware
KoSpeech: Open-Source Toolkit for End-to-End Korean Speech Recognition
https://github.com/sooonwoo/RotNet-OOD
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
https://github.com/soran-ghaderi/torchebm
https://github.com/soumyaxyz/Privacy-Preserving-Federated-Learning
Communication-Efficient Learning of Deep Networks from Decentralized Data
https://github.com/sparvangada/capstone_project
https://github.com/speakerguard/speakerguard
Towards Understanding and Mitigating Audio Adversarial Examples for Speaker Recognition
https://github.com/speech-lab-iitm/pada
Simple and Effective Zero-shot Cross-lingual Phoneme Recognition
https://github.com/speechtranslation/gigas2s
GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio
https://github.com/spiritdjy/MixPaper
Natural Language Processing Advancements By Deep Learning: A Survey
https://github.com/splintr-ml/splintr/blob/master/splintr/dataparallel/isubnet.py
Distributed Learning of Deep Neural Networks using Independent Subnet Training
https://github.com/sra2/spell
STHG: Spatial-Temporal Heterogeneous Graph Learning for Advanced Audio-Visual Diarization
https://github.com/sriharireddypusapati/speech-to-text-wavenet2
https://github.com/srush/annotated-s4
Efficiently Modeling Long Sequences with Structured State Spaces
https://github.com/srvk/eesen
First-Pass Large Vocabulary Continuous Speech Recognition using Bi-Directional Recurrent DNNs
https://github.com/stdereka/liverpool-ion-switching
https://github.com/stefanpantic/asr
https://github.com/stepfun-ai/step-audio
Step-Audio-AQAA: a Fully End-to-End Expressive Large Audio Language Model
https://github.com/stolcke/dover
https://github.com/sunilrdvg/ESOLA
Epoch-Synchronous Overlap-Add (ESOLA) for Time- and Pitch-Scale Modification of Speech Signals
https://github.com/suno-ai/bark
Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers
https://github.com/sw-scot/foco-mlmis
https://github.com/swasun/VQ-VAE-Speech
https://github.com/swatsw/isg_official
https://github.com/switchsyj/adae2ml-xsf
https://github.com/switiz/deepspeech2.pytorch
Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
https://github.com/switiz/las.pytorch
https://github.com/swxhss/densenet_tensorflow
https://github.com/syang1993/gst-tacotron
Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
https://github.com/syiin/ann_cancer_detection
https://github.com/syiin/resnet_cancer_detection
https://github.com/tae-jun/sampleaudio
Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition
https://github.com/takaaki-saeki/ssl_speech_restoration
HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis
https://github.com/tally0818/DARTS
https://github.com/taozitongxue1/11bee-DeepSpeech
First-Pass Large Vocabulary Continuous Speech Recognition using Bi-Directional Recurrent DNNs
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.