https://github.com/Audio-WestlakeU/RCT-Random-Consistency-Training
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
Papers with Code 아카이브
Automatic Speech Recognition (ASR)
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 평가표
- 9
- 보존된 평가 기록
- 20
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/AzizCode92/Listen-Attend-and-Spell-Pytorch
State-of-the-art Speech Recognition With Sequence-to-Sequence Models
https://github.com/Benja1972/Speech_recognition_Tensorflow2.0
Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition
https://github.com/BurnhamG/PyTorch-ScalableFHVAE
Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data
https://github.com/Cbhihe/classify_sound_mobile
Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition
https://github.com/Cross-Caps/STFADE
https://github.com/DataScienceNigeria/Automatic-speech-recognition-ASR-systems-from-googleAI-and-ALSTDI
Personalizing ASR for Dysarthric and Accented Speech with Limited Data
https://github.com/DemisEom/SpecAugment
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
https://github.com/EigenPro/EigenPro
Evaluation of Neural Architectures Trained with Square Loss vs Cross-Entropy in Classification Tasks
https://github.com/FazedAI/OpenSeq2Seq
Mixed-Precision Training for NLP and Speech Recognition with OpenSeq2Seq
https://github.com/FraCorti/Deep_Subnetworks_for_Dynamic_Resource_Constraints
Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition
https://github.com/Fuann/TENET
TENET: A Time-reversal Enhancement Network for Noise-robust ASR
https://github.com/Golesheed/whisper-jasmin-cgn
https://github.com/HLasse/wav2vec_finetune
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
https://github.com/HarunoriKawano/Conformer
Conformer: Convolution-augmented Transformer for Speech Recognition
https://github.com/HeleneFabia/keyword-spotter
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
https://github.com/Hypotheses-Paradise/UADF
https://github.com/JakobCode/UncertaintyInNeuralNetworks_Resources
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
https://github.com/Joshuaoneheart/Fintech-Final-Report
Conformer: Convolution-augmented Transformer for Speech Recognition
https://github.com/JoungheeKim/Non-Attentive-Tacotron
K-Wav2vec 2.0: Automatic Speech Recognition based on Joint Decoding of Graphemes and Syllables
https://github.com/JusperLee/Deep-Clustering-for-Speech-Separation
Single-Channel Multi-Speaker Separation using Deep Clustering
https://github.com/KimJeongSun/SpecAugment_numpy_scipy
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
https://github.com/KrishnaDN/E2E_ASR_Confidence_Estimation
Confidence Estimation for Attention-based Sequence-to-sequence Models for Speech Recognition
https://github.com/LeBenchmark/Interspeech2021
https://github.com/LingweiMeng/Whisper-Sidecar
A Sidecar Separator Can Convert a Single-Talker Speech Recognition System to a Multi-Talker One
https://github.com/MKegler/SpeechVGG
Word-level Embeddings for Cross-Task Transfer Learning in Speech Processing
https://github.com/MS-Mind/MS-Code-01/tree/main/speech_transformer
A Simplified Fully Quantized Transformer for End-to-end Speech Recognition
https://github.com/MagicHub-io/Magic-Data-ASR-SD-Challenge
Conformer: Convolution-augmented Transformer for Speech Recognition
https://github.com/MichaelisTrofficus/spec_augment
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
https://github.com/MindCode-4/code-1/tree/main/tvlt
https://github.com/MindCode-4/code-3/tree/main/encodec
EnCodecMAE: Leveraging neural codecs for universal audio representation learning
https://github.com/MindCode-4/code-3/tree/main/fastspeech2_conformer
Unsupervised Data Selection for TTS: Using Arabic Broadcast News as a Case Study
https://github.com/MindCode-4/code-5/tree/main/tvlt
https://github.com/MindCode-4/code-5/tree/main/wav2vec2_conformer
https://github.com/MindCode-4/code-5/tree/main/whisper
WhisperNER: Unified Open Named Entity and Speech Recognition
https://github.com/MindCode-4/code-9/tree/main/stochastic-attention-head-removal-a-simple
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.