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Accented Speech Recognition
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/mbzuai-nlp/audiojailbreak
공식 코드Audio Jailbreak: An Open Comprehensive Benchmark for Jailbreaking Large Audio-Language Models
https://github.com/mbzuai-nlp/sttatts
공식 코드https://github.com/microsoft/NeuralSpeech
공식 코드PriorGrad: Improving Conditional Denoising Diffusion Models with Data-Dependent Adaptive Prior
https://github.com/microsoft/speecht5
공식 코드SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing
https://github.com/microsoft/unilm
공식 코드Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers
https://github.com/microsoft/unilm/tree/master/LatentLM
공식 코드Multimodal Latent Language Modeling with Next-Token Diffusion
https://github.com/milaniusz/speech2brain2speech
공식 코드Towards Decoding Brain Activity During Passive Listening of Speech
https://github.com/mindslab-ai/assem-vc
공식 코드Assem-VC: Realistic Voice Conversion by Assembling Modern Speech Synthesis Techniques
https://github.com/mindslab-ai/phaseaug
공식 코드PhaseAug: A Differentiable Augmentation for Speech Synthesis to Simulate One-to-Many Mapping
https://github.com/ming024/FastSpeech2
공식 코드Hierarchical Prosody Modeling for Non-Autoregressive Speech Synthesis
https://github.com/mingjund/mapudungun-corpus
공식 코드https://github.com/mozilla/LPCNet
공식 코드LPCNet: Improving Neural Speech Synthesis Through Linear Prediction
https://github.com/ms-dot-k/Lip-to-Speech-Synthesis-in-the-Wild
공식 코드Lip-to-Speech Synthesis in the Wild with Multi-task Learning
https://github.com/ms-dot-k/Visual-Context-Attentional-GAN
공식 코드https://github.com/mu-y/lll-tts
공식 코드Towards Lifelong Learning of Multilingual Text-To-Speech Synthesis
https://github.com/mutiann/byte2speech
공식 코드Multilingual Byte2Speech Models for Scalable Low-resource Speech Synthesis
https://github.com/mutiann/few-shot-transformer-tts
공식 코드Multilingual Byte2Speech Models for Scalable Low-resource Speech Synthesis
https://github.com/naver-ai/RapFlow-TTS
공식 코드RapFlow-TTS: Rapid and High-Fidelity Text-to-Speech with Improved Consistency Flow Matching
https://github.com/naver-ai/facetts
공식 코드Imaginary Voice: Face-styled Diffusion Model for Text-to-Speech
https://github.com/ndkgit339/fastspeech2-filled_pause_speech_synthesis
공식 코드https://github.com/neillu23/cdiffuse
공식 코드Conditional Diffusion Probabilistic Model for Speech Enhancement
https://github.com/neonbjb/tortoise-tts
공식 코드https://github.com/neosapience/editts
공식 코드https://github.com/nesl/asvspoof2019
공식 코드https://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/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/nishantgurunath/source_separation
공식 코드Disentangling Speech and Non-Speech Components for Building Robust Acoustic Models from Found Data
https://github.com/numediart/EmoV-DB
공식 코드The Emotional Voices Database: Towards Controlling the Emotion Dimension in Voice Generation Systems
https://github.com/numediart/LaughterSynthesis
공식 코드Laughter Synthesis: Combining Seq2seq modeling with Transfer Learning
https://github.com/nvidia/bigvgan
공식 코드BigVGAN: A Universal Neural Vocoder with Large-Scale Training
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.