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/navi0105/lyricalignment
공식 코드Adapting pretrained speech model for Mandarin lyrics transcription and alignment
https://github.com/ndkgit339/fastspeech2-filled_pause_speech_synthesis
공식 코드https://github.com/neerajvashistha/online-hate-speech-recog
공식 코드https://github.com/neillu23/cdiffuse
공식 코드Conditional Diffusion Probabilistic Model for Speech Enhancement
https://github.com/nelfproject/NeLF_Transcription_ASR
공식 코드Leveraging Broadcast Media Subtitle Transcripts for Automatic Speech Recognition and Subtitling
https://github.com/neonbjb/tortoise-tts
공식 코드https://github.com/neosapience/editts
공식 코드https://github.com/nervjack2/melhubert
공식 코드https://github.com/nesl/adversarial_audio
공식 코드Did you hear that? Adversarial Examples Against Automatic Speech Recognition
https://github.com/nesl/asvspoof2019
공식 코드https://github.com/nethermanpro/transvip
공식 코드TransVIP: Speech to Speech Translation System with Voice and Isochrony Preservation
https://github.com/neubig/globalutility
공식 코드Systematic Inequalities in Language Technology Performance across the World's Languages
https://github.com/neulab/africanvoices
공식 코드https://github.com/neulab/cmulab
공식 코드CMULAB: An Open-Source Framework for Training and Deployment of Natural Language Processing Models
https://github.com/neulab/xnmt
공식 코드https://github.com/nfurnon/disco
공식 코드https://github.com/nguyen-dv/ViMD_Dataset
공식 코드Multi-Dialect Vietnamese: Task, Dataset, Baseline Models and Challenges
https://github.com/nguyenvulebinh/vietnamese-roberta
공식 코드https://github.com/niger-volta-lti/yoruba-voice
공식 코드https://github.com/nii-yamagishilab/AfriHuBERT
공식 코드AfriHuBERT: A self-supervised speech representation model for African languages
https://github.com/nii-yamagishilab/Generalization_of_CMs_regularizations
공식 코드https://github.com/nii-yamagishilab/multi-speaker-tacotron
공식 코드Can Speaker Augmentation Improve Multi-Speaker End-to-End TTS?
https://github.com/nii-yamagishilab/partial_rank_similarity
공식 코드https://github.com/nii-yamagishilab/project-NN-Pytorch-scripts
공식 코드A Practical Guide to Logical Access Voice Presentation Attack Detection
https://github.com/nii-yamagishilab/self-attention-tacotron
공식 코드https://github.com/nilc-nlp/coraa
공식 코드https://github.com/nilc-nlp/nurc-sp
공식 코드Bringing NURC/SP to Digital Life: the Role of Open-source Automatic Speech Recognition Models
https://github.com/nishantgurunath/source_separation
공식 코드Disentangling Speech and Non-Speech Components for Building Robust Acoustic Models from Found Data
https://github.com/nku-hlt/pb-dsr
공식 코드Enhancing Dysarthric Speech Recognition for Unseen Speakers via Prototype-Based Adaptation
https://github.com/nlokeshiisc/sfedavg-aaai21
공식 코드Game of Gradients: Mitigating Irrelevant Clients in Federated Learning
https://github.com/nnstreamer/nntrainer
공식 코드A New Frontier of AI: On-Device AI Training and Personalization
https://github.com/nphamdinh/transfer-learning-vsr-mouthing-sign-language
공식 코드Transfer Learning from Visual Speech Recognition to Mouthing Recognition in German Sign Language
https://github.com/nttcslab-sp-admin/tedsummary
공식 코드Attention-based Multi-hypothesis Fusion for Speech Summarization
https://github.com/nu-dialogue/antor
공식 코드Adaptive Natural Language Generation for Task-oriented Dialogue via Reinforcement Learning
https://github.com/numediart/EmoV-DB
공식 코드The Emotional Voices Database: Towards Controlling the Emotion Dimension in Voice Generation Systems
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.