https://github.com/caizexin/tf_multispeakerTTS_fc
공식 코드From Speaker Verification to Multispeaker Speech Synthesis, Deep Transfer with Feedback Constraint
Papers with Code 아카이브
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/calclavia/tal-asrd
공식 코드Speech Recognition and Multi-Speaker Diarization of Long Conversations
https://github.com/camma-public/peskavlp
공식 코드Learning Multi-modal Representations by Watching Hundreds of Surgical Video Lectures
https://github.com/camma-public/surgvlp
공식 코드Learning Multi-modal Representations by Watching Hundreds of Surgical Video Lectures
https://github.com/candlewill/RawNet
공식 코드https://github.com/castorini/honk
공식 코드Honk: A PyTorch Reimplementation of Convolutional Neural Networks for Keyword Spotting
https://github.com/celestialshine/longitudinal-chest-x-ray
공식 코드Utilizing Longitudinal Chest X-Rays and Reports to Pre-Fill Radiology Reports
https://github.com/cg563/low-frequency-adversarial
공식 코드https://github.com/changlee0903/d4am
공식 코드D4AM: A General Denoising Framework for Downstream Acoustic Models
https://github.com/chaufanglin/Normal2Whisper
공식 코드Improving Whispered Speech Recognition Performance using Pseudo-whispered based Data Augmentation
https://github.com/chenw20/sgpa
공식 코드https://github.com/chenzhuo1011/libri_css
공식 코드https://github.com/chevalierNoir/A2W-Segmental
공식 코드Whole-Word Segmental Speech Recognition with Acoustic Word Embeddings
https://github.com/chiahsuan156/Spoken-SQuAD
공식 코드https://github.com/chinjouli/dysaug-vc
공식 코드https://github.com/chocobearz/speech_timing
공식 코드Time out of Mind: Generating Rate of Speech conditioned on emotion and speaker
https://github.com/choijeongsoo/lip2speech-unit
공식 코드https://github.com/choijeongsoo/utut
공식 코드Textless Unit-to-Unit training for Many-to-Many Multilingual Speech-to-Speech Translation
https://github.com/choko/learning_to_adapt
공식 코드Learning to adapt: a meta-learning approach for speaker adaptation
https://github.com/chorowski-lab/hcpc
공식 코드Variable-rate hierarchical CPC leads to acoustic unit discovery in speech
https://github.com/chris10m/lip2speech
공식 코드https://github.com/chrischen1023/hint
공식 코드HINT: High-quality INPainting Transformer with Mask-Aware Encoding and Enhanced Attention
https://github.com/chrisemezue/IgboASR
공식 코드https://github.com/ci4r/ASL-Sequential-Dataset
공식 코드ASL Trigger Recognition in Mixed Activity/Signing Sequences for RF Sensor-Based User Interfaces
https://github.com/cic4k/wisebe
공식 코드https://github.com/circleredrain/avger
공식 코드Listening and Seeing Again: Generative Error Correction for Audio-Visual Speech Recognition
https://github.com/ckaestne/seaibib
공식 코드https://github.com/claritychallenge/clarity/tree/main/recipes/cpc1/e029_sheffield
공식 코드https://github.com/claritychallenge/clarity/tree/main/recipes/cpc1/e032_sheffield
공식 코드https://github.com/clovaai/textual-kd-slu
공식 코드Two-stage Textual Knowledge Distillation for End-to-End Spoken Language Understanding
https://github.com/cmu-llab/s3m-aave
공식 코드Self-supervised Speech Representations Still Struggle with African American Vernacular English
https://github.com/cnaigithub/Auto_Tuning_Zeroshot_TTS_and_VC
공식 코드https://github.com/coder-jzq/radka-css
공식 코드Retrieval-Augmented Dialogue Knowledge Aggregation for Expressive Conversational Speech Synthesis
https://github.com/codestar12/Parallel-Independent-Blockwise-Distillation
공식 코드Parallel Blockwise Knowledge Distillation for Deep Neural Network Compression
https://github.com/collectivat-dev/espanyol-ladino-translation
공식 코드Preparing an Endangered Language for the Digital Age: The Case of Judeo-Spanish
https://github.com/collectivat-dev/judeo-espanyol-resources
공식 코드Preparing an Endangered Language for the Digital Age: The Case of Judeo-Spanish
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.