https://github.com/MindSpore-paper-code-3/code2/tree/main/crnn_seq2seq_ocr
The Sequence-to-Sequence Baseline for the Voice Conversion Challenge 2020: Cascading ASR and TTS
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/MindSpore-paper-code-3/code9/tree/main/speech_transformer
A Simplified Fully Quantized Transformer for End-to-end Speech Recognition
https://github.com/MindSpore-scientific-2/code-11/tree/main/ZoneoutRNN
A Comparative Study on Transformer vs RNN in Speech Applications
https://github.com/MindSpore-scientific-2/code-8/tree/main/shallow-rnns
Shallow Fusion of Weighted Finite-State Transducer and Language Model for Text Normalization
https://github.com/MindSpore-scientific/code-1/tree/main/shallow-rnns
Shallow Fusion of Weighted Finite-State Transducer and Language Model for Text Normalization
https://github.com/MiuLab/SlotGated-SLU
https://github.com/NVIDIA/NeMo/tree/main/examples/asr/conf/squeezeformer
Squeezeformer: An Efficient Transformer for Automatic Speech Recognition
https://github.com/Nilanshrajput/Intent_classification
https://github.com/OdysseasKr/online-nilm
Neural NILM: Deep Neural Networks Applied to Energy Disaggregation
https://github.com/OpenSourceAI/sota-server
https://github.com/Orkis-Research/Pytorch-Quaternion-Neural-Networks/tree/28caa7cde240e354fd7b87280450fd233cd494c3/exp/icassp_2019
Bidirectional Quaternion Long-Short Term Memory Recurrent Neural Networks for Speech Recognition
https://github.com/PaddlePaddle/DeepSpeech
https://github.com/Picovoice/speech-to-text-benchmark
https://github.com/Picovoice/stt-benchmark
https://github.com/Priyanshiguptaaa/Intent_Recognition_BERT
https://github.com/Qualcomm-AI-research/bcresnet
Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition
https://github.com/QwenLM/Qwen2.5-Omni
https://github.com/RezisEwig/unity_speech
https://github.com/Riccardo-Vecchi/Pytorch-Quaternion-Neural-Networks
https://github.com/Rishabbh-Sahu/intent_and_slot_classification
https://github.com/Rishit-dagli/Conformer
Conformer: Convolution-augmented Transformer for Speech Recognition
https://github.com/SarthakYadav/audax
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
https://github.com/Snapchat/snapml-templates
Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition
https://github.com/TMarquet/speech_recognition
Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition
https://github.com/TeaPoly/CTC-MWER
Minimum Word Error Rate Training for Attention-based Sequence-to-Sequence Models
https://github.com/TeaPoly/CTC-OptimizedLoss
Minimum Word Error Rate Training for Attention-based Sequence-to-Sequence Models
https://github.com/TeaPoly/Conformer-Athena
Conformer: Convolution-augmented Transformer for Speech Recognition
https://github.com/TensorSpeech/TensorFlowASR
https://github.com/TomVeniat/SANAS
Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition
https://github.com/Tushar-ml/ED-CEC
https://github.com/VP-0822/Video-Keyword-Extractor
https://github.com/Vladimetr/ASR-Knowledge-Transferring
Improving CTC-based speech recognition via knowledge transferring from pre-trained language models
https://github.com/WorldBestProgrammer/KoSpeech
KoSpeech: Open-Source Toolkit for End-to-End Korean Speech Recognition
https://github.com/ZhengkunTian/OpenTransformer
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
https://github.com/agendd/rwkv-asr
An Embarrassingly Simple Approach for LLM with Strong ASR Capacity
https://github.com/ahmedalbahnasawy/spec_torch_kaldi-compatibility
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.