https://github.com/skhu101/tdnn-f_nas
공식 코드Neural Architecture Search For LF-MMI Trained Time Delay Neural Networks
Papers with Code 아카이브
Automatic Speech Recognition (ASR)
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Automatic Speech Recognition (ASR) involves converting spoken language into written text. It is designed to transcribe spoken words into text in real-time, allowing people to communicate with computers, mobile devices, and other technology using their voice. The goal of Automatic Speech Recognition is to accurately transcribe speech, taking into account variations in accent, pronunciation, and speaking style, as well as background noise and other factors that can affect speech quality.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/skit-ai/speech-to-intent-dataset
공식 코드https://github.com/slp-rl/sc-phase
공식 코드A Systematic Comparison of Phonetic Aware Techniques for Speech Enhancement
https://github.com/snipsco/nlu-benchmark
공식 코드https://github.com/snipsco/snips-nlu
공식 코드https://github.com/sooftware/KoSpeech
공식 코드KoSpeech: Open-Source Toolkit for End-to-End Korean Speech Recognition
https://github.com/sparkssss/aequevox
공식 코드AequeVox: Automated Fairness Testing of Speech Recognition Systems
https://github.com/speech-lab-iitm/ccc-wav2vec-2.0
공식 코드https://github.com/speech-lab-iitm/data2vec-aqc
공식 코드data2vec-aqc: Search for the right Teaching Assistant in the Teacher-Student training setup
https://github.com/speechbrain/benchmarks
공식 코드CL-MASR: A Continual Learning Benchmark for Multilingual ASR
https://github.com/speechbrain/speechbrain
공식 코드https://github.com/speechcolab/leaderboard
공식 코드SpeechColab Leaderboard: An Open-Source Platform for Automatic Speech Recognition Evaluation
https://github.com/sreyan88/lipger
공식 코드LipGER: Visually-Conditioned Generative Error Correction for Robust Automatic Speech Recognition
https://github.com/srijith-rkr/whispering-llama
공식 코드Whispering LLaMA: A Cross-Modal Generative Error Correction Framework for Speech Recognition
https://github.com/srvk/how2-dataset
공식 코드How2: A Large-scale Dataset for Multimodal Language Understanding
https://github.com/stefbraun/rnn_benchmarks
공식 코드https://github.com/stellali7/ssl_psr
공식 코드A Quantitative Approach to Understand Self-Supervised Models as Cross-lingual Feature Extractors
https://github.com/steventan0110/star
공식 코드https://github.com/stevenvdeeckt/cgn_cl_dialect
공식 코드Continual Learning for Monolingual End-to-End Automatic Speech Recognition
https://github.com/stevenvdeeckt/online-cl-for-asr
공식 코드Rehearsal-Free Online Continual Learning for Automatic Speech Recognition
https://github.com/stevenvdeeckt/unsupervised-ocl-for-asr
공식 코드Unsupervised Online Continual Learning for Automatic Speech Recognition
https://github.com/sujeetnlp/vedavani
공식 코드Vedavani: A Benchmark Corpus for ASR on Vedic Sanskrit Poetry
https://github.com/sunits/Reverberated_WSJ_2MIX
공식 코드https://github.com/syfengcuhk/jasmin
공식 코드https://github.com/syoung7388/ccl
공식 코드Contrastive and Consistency Learning for Neural Noisy-Channel Model in Spoken Language Understanding
https://github.com/takatomokano/ted_summary
공식 코드Attention-based Multi-hypothesis Fusion for Speech Summarization
https://github.com/tbenst/silent_speech
공식 코드A Cross-Modal Approach to Silent Speech with LLM-Enhanced Recognition
https://github.com/tejas1995/MultimodalASR
공식 코드https://github.com/thaisonngn/pynn/tree/memory/pynn
공식 코드Instant One-Shot Word-Learning for Context-Specific Neural Sequence-to-Sequence Speech Recognition
https://github.com/thu-spmi/ST-NAS
공식 코드https://github.com/thu-spmi/cat
공식 코드https://github.com/thudm/glm-4-voice
공식 코드GLM-4-Voice: Towards Intelligent and Human-Like End-to-End Spoken Chatbot
https://github.com/tianduowang/speech-bt
공식 코드From Tens of Hours to Tens of Thousands: Scaling Back-Translation for Speech Recognition
https://github.com/tomohikonakamura/ica_dsu_espnet
공식 코드Discrete Speech Unit Extraction via Independent Component Analysis
https://github.com/tran-khoa/joint-training-cascaded-st
공식 코드Does Joint Training Really Help Cascaded Speech Translation?
https://github.com/trangham283/asr_preps
공식 코드Assessing the Use of Prosody in Constituency Parsing of Imperfect Transcripts
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.