Papers with Code 아카이브
Speech Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 11
- 평가표
- 236
- 보존된 평가 기록
- 398
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/jinhan/tacotron2-gst
Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
https://github.com/jinhan/tacotron2-vae
Learning latent representations for style control and transfer in end-to-end speech synthesis
https://github.com/jiweibo/imagenet
https://github.com/jkyunnng/happyquokka_system_for_eeg_challenge
https://github.com/jmesich/chestxray
https://github.com/joannahong/Speech-Reconstruction-with-Reminiscent-Sound-via-Visual-Voice-Memory
https://github.com/joannahong/lip-to-speech-synthesis-in-the-wild
Lip-to-Speech Synthesis in the Wild with Multi-task Learning
https://github.com/johnhernandez3/VANGOGHS-EAR
https://github.com/johnhw/differentiable_sorting
https://github.com/johntd54/stanford_car
https://github.com/jojonki/arxiv-clip
Context-Aware Dialog Re-Ranking for Task-Oriented Dialog Systems
https://github.com/jonnor/datascience-master
https://github.com/joseewei/wenet
Unified Streaming and Non-streaming Two-pass End-to-end Model for Speech Recognition
https://github.com/joseluis1061/neuralnilm
Neural NILM: Deep Neural Networks Applied to Energy Disaggregation
https://github.com/joselyn-rodriguez/s3prl
TERA: Self-Supervised Learning of Transformer Encoder Representation for Speech
https://github.com/jovistos/TALAD
Speech Recognition and Multi-Speaker Diarization of Long Conversations
https://github.com/jsiloto/adaptive-cod
Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges
https://github.com/jsleep/wav2mid
An End-to-End Neural Network for Polyphonic Piano Music Transcription
https://github.com/jumon/espnet-1
Improved training of end-to-end attention models for speech recognition
https://github.com/junwang23/deepdirtycodes
A Critical Review of Recurrent Neural Networks for Sequence Learning
https://github.com/jyhan03/icassp22-dataset
AISHELL-1: An Open-Source Mandarin Speech Corpus and A Speech Recognition Baseline
https://github.com/jzmo/espnet
Improved training of end-to-end attention models for speech recognition
https://github.com/k0pch4/big-little-net
Big-Little Net: An Efficient Multi-Scale Feature Representation for Visual and Speech Recognition
https://github.com/k2-fsa/libriheavy
Libri-Light: A Benchmark for ASR with Limited or No Supervision
https://github.com/kabongosalomon/listra
Synchronous Speech Recognition and Speech-to-Text Translation with Interactive Decoding
https://github.com/kadirnar/whisper-plus
https://github.com/kaen2891/kaen2891.github.io
Vocoder-free End-to-End Voice Conversion with Transformer Network
https://github.com/kahnchana/RNN
https://github.com/kaiidams/voice100
Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning
https://github.com/kaiidams/voice100-tts
Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning
https://github.com/kaikai-sk/char-rnn-chinese
Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks
https://github.com/kaistmm/v2sflow
https://github.com/kaituoxu/Listen-Attend-Spell
https://github.com/kalviny/IMTA
Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges
https://github.com/kamperh/bucktsong_eskmeans
A segmental framework for fully-unsupervised large-vocabulary speech recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.