https://github.com/numediart/LaughterSynthesis
공식 코드Laughter Synthesis: Combining Seq2seq modeling with Transfer Learning
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/nvidia-riva/riva-asrlib-decoder
공식 코드GPU-Accelerated WFST Beam Search Decoder for CTC-based Speech Recognition
https://github.com/nvidia/bigvgan
공식 코드BigVGAN: A Universal Neural Vocoder with Large-Scale Training
https://github.com/nyquixt/cascadetta
공식 코드Adaptive Cascading Network for Continual Test-Time Adaptation
https://github.com/nyrahealth/crisperwhisper
공식 코드CrisperWhisper: Accurate Timestamps on Verbatim Speech Transcriptions
https://github.com/ofa-sys/air-bench
공식 코드AIR-Bench: Benchmarking Large Audio-Language Models via Generative Comprehension
https://github.com/ofa-sys/ofa
공식 코드MMSpeech: Multi-modal Multi-task Encoder-Decoder Pre-training for Speech Recognition
https://github.com/ol-mega/ppca
공식 코드Long-term Conversation Analysis: Exploring Utility and Privacy
https://github.com/onnoeberhard/deepspeech
공식 코드Effects of Layer Freezing on Transferring a Speech Recognition System to Under-resourced Languages
https://github.com/onnx/onnx-mlir
공식 코드https://github.com/open-mmlab/amphion
공식 코드Metis: A Foundation Speech Generation Model with Masked Generative Pre-training
https://github.com/open-speech-ekstep/indic-punct
공식 코드https://github.com/openai/whisper
공식 코드https://github.com/orange-opensource/olisia-dstc11
공식 코드https://github.com/orevaahia/yorulect
공식 코드Voices Unheard: NLP Resources and Models for Yorùbá Regional Dialects
https://github.com/osu-starlab/beavertalk
공식 코드BeaverTalk: Oregon State University's IWSLT 2025 Simultaneous Speech Translation System
https://github.com/papercup-open-source/phonological-features
공식 코드Phonological Features for 0-shot Multilingual Speech Synthesis
https://github.com/pariajm/e2e-asr-and-disfluency-removal-evaluator
공식 코드https://github.com/parrotsky/autodice
공식 코드AutoDiCE: Fully Automated Distributed CNN Inference at the Edge
https://github.com/payalmohapatra/emotionshare_acmmm23
공식 코드Effect of Attention and Self-Supervised Speech Embeddings on Non-Semantic Speech Tasks
https://github.com/pberjon/article-accent-recognition
공식 코드Analysis of French Phonetic Idiosyncrasies for Accent Recognition
https://github.com/pengchengguo/espnet
공식 코드Multi-Speaker ASR Combining Non-Autoregressive Conformer CTC and Conditional Speaker Chain
https://github.com/perceptimatic/context-invariance-paper
공식 코드Evaluating context-invariance in unsupervised speech representations
https://github.com/peter-yh-wu/cross-lingual
공식 코드Cross-lingual Transfer for Speech Processing using Acoustic Language Similarity
https://github.com/peter-yh-wu/privacy
공식 코드Understanding the Tradeoffs in Client-side Privacy for Downstream Speech Tasks
https://github.com/peter-yh-wu/speech-privacy
공식 코드Understanding the Tradeoffs in Client-side Privacy for Downstream Speech Tasks
https://github.com/pgurunath/confusion2vec_2.0
공식 코드Confusion2vec 2.0: Enriching Ambiguous Spoken Language Representations with Subwords
https://github.com/pgurunath/slu_confusion2vec
공식 코드https://github.com/piotrkawa/deepfake-whisper-features
공식 코드https://github.com/popcornell/ASRLightningFT
공식 코드Generating Data with Text-to-Speech and Large-Language Models for Conversational Speech Recognition
https://github.com/popcornell/MicRank
공식 코드https://github.com/posenhuang/NPMT
공식 코드https://github.com/potamitis123/chatgpt-in-the-context-of-precision-agriculture-data-analytics
공식 코드ChatGPT in the context of precision agriculture data analytics
https://github.com/pouriagr/deep-cover
공식 코드DeepCover: Advancing RNN Test Coverage and Online Error Prediction using State Machine Extraction
https://github.com/pulp-platform/stream-ebpc
공식 코드EBPC: Extended Bit-Plane Compression for Deep Neural Network Inference and Training Accelerators
https://github.com/pussycat0700/divise
공식 코드DiVISe: Direct Visual-Input Speech Synthesis Preserving Speaker Characteristics And Intelligibility
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.