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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/aigc-audio/audiogpt
https://github.com/akommini/Spoken-Numeric-Digit-detection
Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition
https://github.com/alecokas/lattice_rnn
Bi-Directional Lattice Recurrent Neural Networks for Confidence Estimation
https://github.com/alibaba-damo-academy/FunASR
Conformer: Convolution-augmented Transformer for Speech Recognition
https://github.com/alibaba-damo-academy/spokennlp
Sequence Model with Self-Adaptive Sliding Window for Efficient Spoken Document Segmentation
https://github.com/alicank/Translation-Augmented-LibriSpeech-Corpus
https://github.com/aliyzd95/modified_shemo
https://github.com/amitai1992/AutomatedLipReading
https://github.com/andychinka/dcase-challenge
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
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/as-ideas/deepphonemizer
https://github.com/audio-westlakeu/rct
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
https://github.com/awni/future_speech
https://github.com/aws-samples/aws-lex-noisy-spoken-language-understanding
https://github.com/awslabs/speech-representations
Deep Contextualized Acoustic Representations For Semi-Supervised Speech Recognition
https://github.com/ayyucedemirbas/gcommands_12_classes
Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition
https://github.com/babylonhealth/primock57
https://github.com/biyoml/End-to-End-Mandarin-ASR
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
https://github.com/bozliu/E2E-Keyword-Spotting
Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition
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/chailab-umich/BeyondBinary-ParaphasiaDetection
Seq2seq for Automatic Paraphasia Detection in Aphasic Speech
https://github.com/chia-hsuan-lee/spoken-squad
https://github.com/chiahsuan156/Spoken-SQuAD
https://github.com/chimechallenge/C8DASR-Baseline-NeMo
https://github.com/chorowski-lab/CPC_audio
https://github.com/chorowski-lab/hcpc
https://github.com/common-voice/cv-dataset
https://github.com/cosmoquester/speech-recognition
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
https://github.com/csukuangfj/optimized_transducer
Improving RNN Transducer Modeling for End-to-End Speech Recognition
https://github.com/cui0523/Code6/tree/main/speech_transformer
A Simplified Fully Quantized Transformer for End-to-end Speech Recognition
https://github.com/dabsdamoon/MNIST-Auxiliary-Decoder
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
https://github.com/dapeter/nas-for-kws
Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition
https://github.com/deepspike/snn-for-asr
Deep Spiking Neural Networks for Large Vocabulary Automatic Speech Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.