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/bozliu/E2E-Keyword-Spotting
https://github.com/bradyneal/causal-benchmark
https://github.com/bradyneal/realcause
https://github.com/brightlaboratory/polydl
PolyDL: Polyhedral Optimizations for Creation of High Performance DL primitives
https://github.com/bshall/Tacotron
Location-Relative Attention Mechanisms For Robust Long-Form Speech Synthesis
https://github.com/bshall/hubert
HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units
https://github.com/bsridatta/wav2letter-Swedish
wav2letter++: The Fastest Open-source Speech Recognition System
https://github.com/cadia-lvl/fastspeech2
https://github.com/caillonantoine/waveflow
https://github.com/carbonati/fl-zoo
Communication-Efficient Learning of Deep Networks from Decentralized Data
https://github.com/carlini/audio_adversarial_examples
Audio Adversarial Examples: Targeted Attacks on Speech-to-Text
https://github.com/cassandra-lehmann/ensemble_methods_ASR_transcripts
https://github.com/castorini/deebert
Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges
https://github.com/castorini/earlyexiting-monobert
Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges
https://github.com/cchinchristopherj/Concert-of-Whales
SampleRNN: An Unconditional End-to-End Neural Audio Generation Model
https://github.com/cecile-hi/regularized-adaptive-weight-modification
What to Remember: Self-Adaptive Continual Learning for Audio Deepfake Detection
https://github.com/cges60809/DenseNet
https://github.com/chailab-umich/BeyondBinary-ParaphasiaDetection
Seq2seq for Automatic Paraphasia Detection in Aphasic Speech
https://github.com/chia-hsuan-lee/spoken-squad
https://github.com/chimechallenge/C8DASR-Baseline-NeMo
https://github.com/chl218/DCNN-on-FPGA
A Design Methodology for Efficient Implementation of Deconvolutional Neural Networks on an FPGA
https://github.com/choiHkk/Transformer-TTS
Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions
https://github.com/chorowski-lab/CPC_audio
https://github.com/christopher-beckham/annotated-conditional-diffusion
Conditional Diffusion Probabilistic Model for Speech Enhancement
https://github.com/chriswegmann/drone_steering
Beyond Temporal Pooling: Recurrence and Temporal Convolutions for Gesture Recognition in Video
https://github.com/ciaua/score_lyrics_free_svg
https://github.com/cmasch/densenet
https://github.com/cmu-llab/meloni-2021-reimplementation
Differentiable Allophone Graphs for Language-Universal Speech Recognition
https://github.com/cnlinxi/style-token_tacotron2
Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
https://github.com/code-implementation1/Code1/tree/main/densenet
https://github.com/codetendolkar/tacotron-2-explained
Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions
https://github.com/colaprograms/speechify
https://github.com/collabora/whisperlive
https://github.com/collabora/whisperspeech
https://github.com/common-voice/cv-dataset
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.