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/alpharol/Taco_Collection
Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions
https://github.com/amiannn/espnet
Reproducing Whisper-Style Training Using an Open-Source Toolkit and Publicly Available Data
https://github.com/amikey/pytorch-kaldi
https://github.com/amitai1992/AutomatedLipReading
https://github.com/ammesatyajit/VideoBERT
VideoBERT: A Joint Model for Video and Language Representation Learning
https://github.com/anandaswarup/TTS
https://github.com/anandaswarup/phrase_break_prediction
https://github.com/anandaswarup/rnn-tts
https://github.com/anandaswarup/waveRNN
https://github.com/anandharaju/Basic_TCN
https://github.com/andabi/deep-voice-conversion
https://github.com/andabi/voice-vector
https://github.com/andreasveit/densenet-pytorch
https://github.com/andrewkkan/fathom
Communication-Efficient Learning of Deep Networks from Decentralized Data
https://github.com/andychinka/dcase-challenge
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
https://github.com/anjandeepsahni/automatic_speech_recognition
https://github.com/anssssss/Vietnamese-Speech-Recognition
https://github.com/anti-antares/Deep_Learning_Project_4
https://github.com/anton-jeran/MESH2IR
FAST-RIR: Fast neural diffuse room impulse response generator
https://github.com/anton-jeran/Speech2RIR
FAST-RIR: Fast neural diffuse room impulse response generator
https://github.com/anuragreddygv323/Important-stuff
Use of Deep Learning in Modern Recommendation System: A Summary of Recent Works
https://github.com/apoorvagnihotri/big-little-net
Big-Little Net: An Efficient Multi-Scale Feature Representation for Visual and Speech Recognition
https://github.com/areffarhadi/Whisper_fine_tuning_ASR
Leveraging Self-Supervised Models for Automatic Whispered Speech Recognition
https://github.com/areffarhadi/mamba-asr
https://github.com/arnavdodiedo/DenseNet-Fashion-MNIST
https://github.com/arnavdodiedo/DenseNet-MNIST
https://github.com/around-star/Speech-Recognition
Recurrent Neural Network Transducer for Audio-Visual Speech Recognition
https://github.com/arshadshk/Position-Prediction-Pretraining
https://github.com/as-ideas/TransformerTTS
https://github.com/as-ideas/deepforcedaligner
https://github.com/as-ideas/deepphonemizer
https://github.com/asad-62/IVP-DNN
https://github.com/ashishpatel26/tcn-keras-Examples
https://github.com/ashutosh1919/data2vec-pytorch
data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language
https://github.com/ashwin9999/Capstone-Speech-to-SQL
Wav2Letter: an End-to-End ConvNet-based Speech Recognition System
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.