https://github.com/0xrutvij/slothspeech
공식 코드SlothSpeech: Denial-of-service Attack Against Speech Recognition Models
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/193746/VHASR
공식 코드VHASR: A Multimodal Speech Recognition System With Vision Hotwords
https://github.com/9yte/VenoMave
공식 코드https://github.com/AI4Bharat/indicSUPERB
공식 코드IndicSUPERB: A Speech Processing Universal Performance Benchmark for Indian languages
https://github.com/Alexander-H-Liu/End-to-end-ASR-Pytorch
공식 코드https://github.com/AmirAbaskohi/Automatic-Speech-recognition-for-Speech-Assessment-of-Persian-Preschool-Children
공식 코드Automatic Speech Recognition for Speech Assessment of Persian Preschool Children
https://github.com/AsrAttackSok/asr-attack-sok
공식 코드SoK: A Modularized Approach to Study the Security of Automatic Speech Recognition Systems
https://github.com/Audio-WestlakeU/CleanMel
공식 코드CleanMel: Mel-Spectrogram Enhancement for Improving Both Speech Quality and ASR
https://github.com/Audio-WestlakeU/UMA-ASR
공식 코드https://github.com/Audio-WestlakeU/VINP
공식 코드https://github.com/BUTSpeechFIT/TS-ASR-Whisper
공식 코드DiCoW: Diarization-Conditioned Whisper for Target Speaker Automatic Speech Recognition
https://github.com/BriansIDP/RTLM
공식 코드Transformer Language Models with LSTM-based Cross-utterance Information Representation
https://github.com/ClovaAI/ClovaCall
공식 코드https://github.com/CoraJung/flexible-input-slu
공식 코드Speak or Chat with Me: End-to-End Spoken Language Understanding System with Flexible Inputs
https://github.com/DanchengLiu/FASA
공식 코드https://github.com/DeutscheKI/tevr-asr-tool
공식 코드TEVR: Improving Speech Recognition by Token Entropy Variance Reduction
https://github.com/GavinGuan95/Punctuator.Pytorch
공식 코드https://github.com/GenerateAI/LibriCrowd
공식 코드https://github.com/HPI-DeepLearning/crnn-lid
공식 코드Language Identification Using Deep Convolutional Recurrent Neural Networks
https://github.com/HearingTechnology/CoNNear_cochlea
공식 코드https://github.com/IS2AI/MultilingualASR
공식 코드A Study of Multilingual End-to-End Speech Recognition for Kazakh, Russian, and English
https://github.com/IS2AI/Uzbek_ASR
공식 코드USC: An Open-Source Uzbek Speech Corpus and Initial Speech Recognition Experiments
https://github.com/JackKelly/neuralnilm_prototype
공식 코드Neural NILM: Deep Neural Networks Applied to Energy Disaggregation
https://github.com/Jiang-Yidi/TransformerDistillation-SLU
공식 코드Knowledge Distillation from BERT Transformer to Speech Transformer for Intent Classification
https://github.com/JunwenBai/DAPC
공식 코드Representation Learning for Sequence Data with Deep Autoencoding Predictive Components
https://github.com/Kohei-Matsuura/Non-parallel-VC-on-Mboshi
공식 코드Generative Adversarial Training Data Adaptation for Very Low-resource Automatic Speech Recognition
https://github.com/KurdishBLARK/BD-4SK-ASR
공식 코드Kurdish (Sorani) Speech to Text: Presenting an Experimental Dataset
https://github.com/LeonWlw/asr_blockformer
공식 코드Improving Mandarin Speech Recogntion with Block-augmented Transformer
https://github.com/Lhx94As/reducing_language_confusion
공식 코드https://github.com/MarkWuNLP/SemanticMask
공식 코드Semantic Mask for Transformer based End-to-End Speech Recognition
https://github.com/MaxSob/ASRNeuralClassification
공식 코드Hybrid phonetic-neural model for correction in speech recognition systems
https://github.com/MingLunHan/CIF-PyTorch
공식 코드https://github.com/NVIDIA/NeMo
공식 코드Shallow Fusion of Weighted Finite-State Transducer and Language Model for Text Normalization
https://github.com/NVIDIA/NeMo/tree/main/nemo_text_processing/inverse_text_normalization
공식 코드NeMo Inverse Text Normalization: From Development To Production
https://github.com/NVIDIA/OpenSeq2Seq
공식 코드Mixed-Precision Training for NLP and Speech Recognition with OpenSeq2Seq
https://github.com/NaoyukiKanda/LibriSpeechMix
공식 코드Minimum Bayes Risk Training for End-to-End Speaker-Attributed ASR
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.