https://github.com/mlxu995/multihead-LDSA
공식 코드Transformer-based End-to-End Speech Recognition with Local Dense Synthesizer Attention
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/mnabihali/ASR-FL
공식 코드https://github.com/mobvoi/lstm_ctc
공식 코드End-To-End Speech Recognition Using A High Rank LSTM-CTC Based Model
https://github.com/mobvoi/wenet
공식 코드WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit
https://github.com/moncefbenaicha/spoken-ner
공식 코드Leveraging Cross-Lingual Transfer Learning in Spoken Named Entity Recognition Systems
https://github.com/monomest/thesis
공식 코드Improving Children's Speech Recognition by Fine-tuning Self-supervised Adult Speech Representations
https://github.com/moonshotai/kimi-audio
공식 코드https://github.com/moorethreads/mooer
공식 코드MooER: LLM-based Speech Recognition and Translation Models from Moore Threads
https://github.com/morenolaquatra/flanec
공식 코드https://github.com/morenolaquatra/ger4dys
공식 코드Exploring Generative Error Correction for Dysarthric Speech Recognition
https://github.com/mozilla/LPCNet
공식 코드LPCNet: Improving Neural Speech Synthesis Through Linear Prediction
https://github.com/mpc001/Visual_Speech_Recognition_for_Multiple_Languages
공식 코드Visual Speech Recognition for Multiple Languages in the Wild
https://github.com/mpc001/auto_avsr
공식 코드Auto-AVSR: Audio-Visual Speech Recognition with Automatic Labels
https://github.com/mravanelli/SincNet
공식 코드Speech and Speaker Recognition from Raw Waveform with SincNet
https://github.com/mravanelli/pytorch-kaldi
공식 코드https://github.com/mravanelli/theano-kaldi-rnn
공식 코드Improving speech recognition by revising gated recurrent units
https://github.com/ms-dot-k/AVSR
공식 코드https://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/User-dependent-Padding
공식 코드https://github.com/ms-dot-k/Visual-Context-Attentional-GAN
공식 코드https://github.com/mtkresearch/clairaudience
공식 코드Zero-shot Domain-sensitive Speech Recognition with Prompt-conditioning Fine-tuning
https://github.com/mtkresearch/generative-fusion-decoding
공식 코드https://github.com/mtran14/dp_w2v2
공식 코드A Speech Representation Anonymization Framework via Selective Noise Perturbation
https://github.com/mu-y/diarist
공식 코드DiariST: Streaming Speech Translation with Speaker Diarization
https://github.com/mu-y/lll-tts
공식 코드Towards Lifelong Learning of Multilingual Text-To-Speech Synthesis
https://github.com/muelitas/keywordrec
공식 코드https://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/myHaiven/data-collection
공식 코드BERSting at the Screams: A Benchmark for Distanced, Emotional and Shouted Speech Recognition
https://github.com/mynlp/speechparser
공식 코드https://github.com/n8rob/multilingual_tts_augmentation
공식 코드https://github.com/natsuooo/llm-ger
공식 코드LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context
https://github.com/navana-tech/baseline_recipe_is21s_indic_asr_challenge
공식 코드Multilingual and code-switching ASR challenges for low resource Indian languages
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/naver-ai/usdm
공식 코드Paralinguistics-Aware Speech-Empowered Large Language Models for Natural Conversation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.