https://github.com/youngeun1209/neurotalk
공식 코드Towards Voice Reconstruction from EEG during Imagined Speech
Papers with Code 아카이브
Automatic Speech Recognition (ASR)
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Automatic Speech Recognition (ASR) involves converting spoken language into written text. It is designed to transcribe spoken words into text in real-time, allowing people to communicate with computers, mobile devices, and other technology using their voice. The goal of Automatic Speech Recognition is to accurately transcribe speech, taking into account variations in accent, pronunciation, and speaking style, as well as background noise and other factors that can affect speech quality.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/yuchen005/dpsl-asr
공식 코드Interactive Feature Fusion for End-to-End Noise-Robust Speech Recognition
https://github.com/yuchen005/gila
공식 코드Cross-Modal Global Interaction and Local Alignment for Audio-Visual Speech Recognition
https://github.com/yuchen005/gradient-remedy
공식 코드Gradient Remedy for Multi-Task Learning in End-to-End Noise-Robust Speech Recognition
https://github.com/yuchen005/robustger
공식 코드Large Language Models are Efficient Learners of Noise-Robust Speech Recognition
https://github.com/yuchen005/star-adapt
공식 코드Self-Taught Recognizer: Toward Unsupervised Adaptation for Speech Foundation Models
https://github.com/yuwchen/baspro
공식 코드BASPRO: a balanced script producer for speech corpus collection based on the genetic algorithm
https://github.com/yygle/HybridFormer
공식 코드HYBRIDFORMER: improving SqueezeFormer with hybrid attention and NSR mechanism
https://github.com/zaharah/ood_audio
공식 코드On Out-of-Distribution Detection for Audio with Deep Nearest Neighbors
https://github.com/zelokuo/VPIDM
공식 코드https://github.com/zexupan/avse_hybrid_loss
공식 코드A Hybrid Continuity Loss to Reduce Over-Suppression for Time-domain Target Speaker Extraction
https://github.com/zhu-han/castle
공식 코드Boosting Cross-Domain Speech Recognition with Self-Supervision
https://github.com/zinengtang/tvlt
공식 코드https://github.com/znlp/zero-shot-st
공식 코드Discrete Cross-Modal Alignment Enables Zero-Shot Speech Translation
https://github.com/zombbie/entity-synthetic-dataset
공식 코드Towards Contextual Spelling Correction for Customization of End-to-end Speech Recognition Systems
https://github.com/zorikg/red-ace
공식 코드RED-ACE: Robust Error Detection for ASR using Confidence Embeddings
https://github.com/zqs01/data2vecnoisy
공식 코드https://github.com/zqs01/multi-channel-wav2vec2
공식 코드Multichannel AV-wav2vec2: A Framework for Learning Multichannel Multi-Modal Speech Representation
https://github.com/zslin177/copyne
공식 코드https://github.com/zted/sbvqa
공식 코드https://github.com/zyzpower/gradprune_speaker
공식 코드https://gitlab.com/kavyamanohar/ml-subword-asr
공식 코드Syllable Subword Tokens for Open Vocabulary Speech Recognition in Malayalam
https://gitlab.com/sburud/master
공식 코드BERT Attends the Conversation: Improving Low-Resource Conversational ASR
https://huggingface.co/fxtentacle/wav2vec2-xls-r-1b-tevr
공식 코드TEVR: Improving Speech Recognition by Token Entropy Variance Reduction
https://huggingface.co/utter-project/mHuBERT-147
공식 코드https://github.com/0xSameer/ast
Pre-training on high-resource speech recognition improves low-resource speech-to-text translation
https://github.com/193746/jsrsl
VHASR: A Multimodal Speech Recognition System With Vision Hotwords
https://github.com/1hb6s7t/contrib
https://github.com/2023-MindSpore-1/ms-code-15/tree/main/speech_transformer
A Simplified Fully Quantized Transformer for End-to-end Speech Recognition
https://github.com/2023-MindSpore-4/Code-5/tree/main/I3D
https://github.com/2023-MindSpore-4/Code8/tree/main/crnn_seq2seq_ocr
The Sequence-to-Sequence Baseline for the Voice Conversion Challenge 2020: Cascading ASR and TTS
https://github.com/2sang/OOD-baseline
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
https://github.com/30stomercury/Automatic_Speech_Recognition
State-of-the-art Speech Recognition With Sequence-to-Sequence Models
https://github.com/Abdallah-Hesham99/tell_your_motor_speed
Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition
https://github.com/Alpe6825/Unicornn
Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition
https://github.com/AmirmohammadRostami/KeywordsSpotting-EfficientNet-A0
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.