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
Papers with Code 아카이브
Speech Recognition
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- 평가표
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Speech Recognition is the task of converting spoken language into text. It involves recognizing the words spoken in an audio recording and transcribing them into a written format. The goal is to accurately transcribe the speech in real-time or from recorded audio, taking into account factors such as accents, speaking speed, and background noise.
<span style="color:grey; opacity: 0.6">( Image credit: SpecAugment )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Orlllem/wav2vec2-fairseq-pt-br
https://github.com/PaddlePaddle/DeepSpeech
https://github.com/PaddlePaddle/FleetX/blob/develop/examples/resnet/train_fleet_static_dgc.py
Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training
https://github.com/PaddlePaddle/PaddleClas
https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/vctk/ernie_sat
A$^3$T: Alignment-Aware Acoustic and Text Pretraining for Speech Synthesis and Editing
https://github.com/PaddlePaddle/models
Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
https://github.com/Pandade1997/tera_asvproof
TERA: Self-Supervised Learning of Transformer Encoder Representation for Speech
https://github.com/PeihaoChen/regnet
https://github.com/PeiyanFlying/https-github.com-PeiyanFlying-pytorch-kaldi
https://github.com/PengdaLiu/LAS-SpeechRecognition
https://github.com/PhilippeNguyen/keras_wavenet
https://github.com/Picovoice/speech-to-text-benchmark
https://github.com/Picovoice/stt-benchmark
https://github.com/Priyanshiguptaaa/Intent_Recognition_BERT
https://github.com/QData/TextAttack/blob/master/textattack/transformations/sentence_transformations/back_transcription.py
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/RRoundTable/OceanLitter_dataset_generator
https://github.com/RaghavHub/rekuten_hack
https://github.com/RajdeepBiswas/Manufacturing-Quality-Inspection
https://github.com/RajuGudhe/Cancer_Tissue_Detection
https://github.com/RameshBattu/Convolutional-Neural-Network-on-CIFAR-10-DataSet
https://github.com/RamsteinWR/wavenet-master
https://github.com/RashadGarayev/TRSpeech-to-text
https://github.com/RayeRen/RayeRen
FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
https://github.com/Rayhane-mamah/Tacotron-2
Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions
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/RoganInglis/AudioLM/tree/main/src/models
https://github.com/RohanAsnani/capstone_project_machine_learning-nanodegree
https://github.com/Rongjiehuang/Multiband-WaveRNN
https://github.com/RotemMayo/NAF
https://github.com/RuntimeRacer/rtvc-gcloud
Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.