https://github.com/ristea/ccmt
공식 코드Cascaded Cross-Modal Transformer for Audio-Textual Classification
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/rita-nlp/italic
공식 코드https://github.com/rithiksachdev/PostASR-Correction-SLT2024
공식 코드Evolutionary Prompt Design for LLM-Based Post-ASR Error Correction
https://github.com/rizar/attention-lvcsr
공식 코드End-to-End Attention-based Large Vocabulary Speech Recognition
https://github.com/robflynnyh/Self-Train-Before-You-Transcribe
공식 코드https://github.com/robflynnyh/long-context-asr
공식 코드https://github.com/roblox/smoothcache
공식 코드SmoothCache: A Universal Inference Acceleration Technique for Diffusion Transformers
https://github.com/roger-tseng/speech-parsing
공식 코드Cascading and Direct Approaches to Unsupervised Constituency Parsing on Spoken Sentences
https://github.com/rohan-chaudhury/humane-speech-synthesis-through-zero-shot-emotion-and-disfluency-generation
공식 코드Humane Speech Synthesis through Zero-Shot Emotion and Disfluency Generation
https://github.com/roholazandie/ryan-tts
공식 코드RyanSpeech: A Corpus for Conversational Text-to-Speech Synthesis
https://github.com/roman-vygon/triplet_loss_kws
공식 코드Learning Efficient Representations for Keyword Spotting with Triplet Loss
https://github.com/roudimit/whisper-flamingo
공식 코드https://github.com/rtaori/Black-Box-Audio
공식 코드https://github.com/ruanvdmerwe/triplet-entropy-loss
공식 코드Triplet Entropy Loss: Improving The Generalisation of Short Speech Language Identification Systems
https://github.com/rub-ksv/adrenaline
공식 코드Exploiting Attention-based Sequence-to-Sequence Architectures for Sound Event Localization
https://github.com/rub-ksv/avtrack
공식 코드Audiovisual Speaker Tracking using Nonlinear Dynamical Systems with Dynamic Stream Weights
https://github.com/rub-ksv/federated-learning-asr
공식 코드https://github.com/rub-ksv/spatial-stream-weights
공식 코드https://github.com/rub-ksv/uncertaintyASR
공식 코드Detecting Adversarial Examples for Speech Recognition via Uncertainty Quantification
https://github.com/rub-syssec/dompteur
공식 코드https://github.com/ruizhecao96/cmgan
공식 코드https://github.com/rwth-i6/returnn
공식 코드RETURNN as a Generic Flexible Neural Toolkit with Application to Translation and Speech Recognition
https://github.com/rwth-i6/returnn-experiments
공식 코드Generating Synthetic Audio Data for Attention-Based Speech Recognition Systems
https://github.com/rwth-i6/returnn-experiments/tree/master/2021-transducer
공식 코드Librispeech Transducer Model with Internal Language Model Prior Correction
https://github.com/ryderling/adversarial-attacks-and-defenses-for-windows-pe-malware-detection
공식 코드Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Art
https://github.com/ryujimorita/tokyo_whisperers
공식 코드Efficient Adaptation of Multilingual Models for Japanese ASR
https://github.com/s1603602/attention_head_removal
공식 코드https://github.com/s3prl/s3prl
공식 코드TERA: Self-Supervised Learning of Transformer Encoder Representation for Speech
https://github.com/sabeesh90/MosaicML_Augmentations_Efficient_Deep_Learning_MLDS_2022
공식 코드https://github.com/sadhusamik/speech_recognition_tools
공식 코드Radically Old Way of Computing Spectra: Applications in End-to-End ASR
https://github.com/sage-khan/code-switched-noisy-urdu-asr
공식 코드https://github.com/sailordiary/deep-face-vsr
공식 코드Can We Read Speech Beyond the Lips? Rethinking RoI Selection for Deep Visual Speech Recognition
https://github.com/sakshishukla1996/SpeechTopSeg
공식 코드Advancing Topic Segmentation of Broadcasted Speech with Multilingual Semantic Embeddings
https://github.com/salah-zaiem/PL-groupselection
공식 코드Pretext Tasks selection for multitask self-supervised speech representation learning
https://github.com/salah-zaiem/Pseudo-Label-Selection
공식 코드https://github.com/salah-zaiem/speeding_inferences
공식 코드Fine-tuning Strategies for Faster Inference using Speech Self-Supervised Models: A Comparative Study
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.