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Speech Recognition
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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/voidism/NAR-ST
공식 코드https://github.com/w-wu/steer
공식 코드Integrating Emotion Recognition with Speech Recognition and Speaker Diarisation for Conversations
https://github.com/walker-hyf/ecss
공식 코드https://github.com/walker-hyf/fctalker
공식 코드FCTalker: Fine and Coarse Grained Context Modeling for Expressive Conversational Speech Synthesis
https://github.com/walker-hyf/mntts
공식 코드MnTTS: An Open-Source Mongolian Text-to-Speech Synthesis Dataset and Accompanied Baseline
https://github.com/wangfangyuan/SChunk-Encoder
공식 코드Shifted Chunk Encoder for Transformer Based Streaming End-to-End ASR
https://github.com/wanghelin1997/duta-vc
공식 코드https://github.com/wangkenpu/rsrgan
공식 코드https://github.com/wangmengzhi/Lightweight-Transducer
공식 코드https://github.com/wangyu09/exkaldi-rt
공식 코드ExKaldi-RT: A Real-Time Automatic Speech Recognition Extension Toolkit of Kaldi
https://github.com/wannaphong/thai_commonvoice_dataset
공식 코드https://github.com/warmcongee/sdumc
공식 코드https://github.com/warnikchow/kosp2e
공식 코드https://github.com/weizou52/robustness_analysis_asr
공식 코드Analyzing Robustness of End-to-End Neural Models for Automatic Speech Recognition
https://github.com/wenet-e2e/wenet
공식 코드WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit
https://github.com/wenet-e2e/wenetspeech
공식 코드WenetSpeech: A 10000+ Hours Multi-domain Mandarin Corpus for Speech Recognition
https://github.com/wenet-e2e/wesep
공식 코드WeSep: A Scalable and Flexible Toolkit Towards Generalizable Target Speaker Extraction
https://github.com/wiebket/sveva-fair
공식 코드SVEva Fair: A Framework for Evaluating Fairness in Speaker Verification
https://github.com/winddori2002/dex-tts
공식 코드DEX-TTS: Diffusion-based EXpressive Text-to-Speech with Style Modeling on Time Variability
https://github.com/wisdomikezogwo/quilt1m
공식 코드https://github.com/wnhsu/ScalableFHVAE
공식 코드Scalable Factorized Hierarchical Variational Autoencoder Training
https://github.com/wojzaremba/lstm
공식 코드https://github.com/wonjune-kang/llm-speech-summarization
공식 코드Prompting Large Language Models with Audio for General-Purpose Speech Summarization
https://github.com/wonjune-kang/lvc-vc
공식 코드End-to-End Zero-Shot Voice Conversion with Location-Variable Convolutions
https://github.com/world1tree/SpeechTranslationRefinement
공식 코드https://github.com/wutong8023/speechre
공식 코드https://github.com/wxzyd123/pivotal_objective_perturbation
공식 코드Mitigating Unauthorized Speech Synthesis for Voice Protection
https://github.com/wxzyd123/safespeech
공식 코드SafeSpeech: Robust and Universal Voice Protection Against Malicious Speech Synthesis
https://github.com/xcyu-0903/rose
공식 코드https://github.com/xi-j/Mamba-TasNet
공식 코드https://github.com/xi-j/mamba-asr
공식 코드https://github.com/xianchao-wu/wenet-deep-sparse-conformer
공식 코드https://github.com/xiangli2022/cm-tts
공식 코드https://github.com/xiaoleihuang/Multilingual_Fairness_LREC
공식 코드Multilingual Twitter Corpus and Baselines for Evaluating Demographic Bias in Hate Speech Recognition
https://github.com/xiaoxue1117/altp_chords_lyrics
공식 코드Music-robust Automatic Lyrics Transcription of Polyphonic Music
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.