Papers with Code 아카이브
Accented Speech Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ucsd-dsc-arts/dsc160-final-dsc160-final-group19
https://github.com/umd-fire-coml/2020-Speech-Recognition
Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
https://github.com/vicky-hnk/time-flex
https://github.com/victor369basu/End2EndAutomaticSpeechRecognition
Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
https://github.com/vincentherrmann/pytorch-wavenet
https://github.com/vincenzo-scotti/tacotron2
Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions
https://github.com/vinusankars/ESOLA
Epoch-Synchronous Overlap-Add (ESOLA) for Time- and Pitch-Scale Modification of Speech Signals
https://github.com/vohoaiviet/voice-vector
https://github.com/wangys102/MindSpore/tree/main/DeepSpeech2
Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
https://github.com/wataru-nakata/fastspeech2-jsut
FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
https://github.com/weilinie/JARE
Towards a Better Understanding and Regularization of GAN Training Dynamics
https://github.com/whisperspeech/whisperspeech
https://github.com/xcmyz/FastSpeech
Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions
https://github.com/xinleiniu/latentoptimalpathsbayesiandp
RyanSpeech: A Corpus for Conversational Text-to-Speech Synthesis
https://github.com/xinshengwang/Tacotron-pytorch
Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions
https://github.com/yanggeng1995/EATS
https://github.com/yanggeng1995/FB-MelGAN
MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis
https://github.com/yanggeng1995/GAN-TTS
https://github.com/yanggeng1995/WaveGlow
WaveGlow: A Flow-based Generative Network for Speech Synthesis
https://github.com/yanggeng1995/subband_WaveRNN
DurIAN: Duration Informed Attention Network For Multimodal Synthesis
https://github.com/yanggeng1995/vae_tacotron
Learning latent representations for style control and transfer in end-to-end speech synthesis
https://github.com/yangyucheng000/University/tree/main/model-2/fastspeech2_conformer
FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
https://github.com/yangyucheng000/University/tree/main/model-3/seamless_m4t
SeamlessM4T: Massively Multilingual & Multimodal Machine Translation
https://github.com/yangyucheng000/University/tree/main/model-3/speecht5
SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing
https://github.com/yebiny/DepthOfAnaesthesia_eeg
https://github.com/yl4579/StyleTTS2
https://github.com/ylacombe/finetune-hf-vits
https://github.com/yoshifumi-nakano/visual-text-to-speech
FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
https://github.com/yxlu-0102/MP-SENet
Source-Filter-Based Generative Adversarial Neural Vocoder for High Fidelity Speech Synthesis
https://github.com/yzyouzhang/AIR-ASVspoof
SAMO: Speaker Attractor Multi-Center One-Class Learning for Voice Anti-Spoofing
https://github.com/zhangbo2008/fastSpeeck2_chinese_train
FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
https://github.com/zhihaozhang97/ru-ai
Text-Free Image-to-Speech Synthesis Using Learned Segmental Units
https://github.com/zhong110020/Tensorflow-TCN
https://github.com/zhong110020/keras-tcn
https://github.com/zll1996/TCN
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.