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/minhtriet/gridworld
A Simple Way to Initialize Recurrent Networks of Rectified Linear Units
https://github.com/mink007/Emotion_Recog_Deep_Neural_Networks/blob/master/CS231N_Project_Presentation.pptx
Emotion Recognition in Audio and Video Using Deep Neural Networks
https://github.com/miraclewkf/densenet
https://github.com/mitsu-h/deepvoice3
Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning
https://github.com/mkotha/WaveRNN
https://github.com/mkunes/w2v2_audioframeclassification
Detection of Prosodic Boundaries in Speech Using Wav2Vec 2.0
https://github.com/mlvccn/bmtc_transferattackvid
https://github.com/mnm-rnd/elsa-voice-asr
https://github.com/modelhub-ai/densenet
https://github.com/modelscope/FunCodec
LauraGPT: Listen, Attend, Understand, and Regenerate Audio with GPT
https://github.com/modelscope/modelscope
https://github.com/mohamad-m2/federated-learning
Communication-Efficient Learning of Deep Networks from Decentralized Data
https://github.com/mozilla/DeepSpeech
https://github.com/mozilla/STT
https://github.com/mozilla/TTS
https://github.com/mpc001/end-to-end-Lipreading
https://github.com/mravanelli/pySpeechRev
The DIRHA-English corpus and related tasks for distant-speech recognition in domestic environments
https://github.com/mrkolarik/3d-brain-segmentation
https://github.com/ms903-github/ACAL-pytorch
Augmented Cyclic Adversarial Learning for Low Resource Domain Adaptation
https://github.com/msalhab96/Conformer
Conformer: Convolution-augmented Transformer for Speech Recognition
https://github.com/msalhab96/Listen-Attend-and-Spell
https://github.com/msalhab96/SpeeQ
https://github.com/mtresearcher/FastSpeech2
FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
https://github.com/muditrastogi/chestai
https://github.com/mukeshv0/ParallelWaveGAN
https://github.com/myrtleSoftware/deepspeech
https://github.com/nadavlab/federatedbehrt
Communication-Efficient Learning of Deep Networks from Decentralized Data
https://github.com/nafiuny/ICRCycleGAN-VC
Statistical Parametric Speech Synthesis Incorporating Generative Adversarial Networks
https://github.com/nanzhaogang/contrib/tree/master/application/similarity-of-neural-network-representations-revisited
https://github.com/nashawnch/Computer-Vision-FerrariDetective
https://github.com/nashawnch/ComputerVision-FerrariDetective
https://github.com/nateanl/wpd-plus-plus
WPD++: An Improved Neural Beamformer for Simultaneous Speech Separation and Dereverberation
https://github.com/nbansal90/bAbi_QA
https://github.com/ncsoft/phonmatchnet
Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition
https://github.com/ndk03/Accent-Preserving-Voice-Cloning-using-SV2TTS/tree/master/Speech-Modeling-master
Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.