Papers with Code 아카이브

Automatic Speech Recognition (ASR)

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 1
평가표: 9
보존된 평가 기록: 20

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/Niger-Volta-LTI/yoruba-adr

공식 코드

Attentive Sequence-to-Sequence Learning for Diacritic Restoration of Yorùbá Language Text

https://github.com/Open-Speech-EkStep/vakyansh-wav2vec2-experimentation

공식 코드

Speaker Recognition in the Wild

https://github.com/OrcusCZ/NNAcousticModeling

공식 코드

Recurrent DNNs and its Ensembles on the TIMIT Phone Recognition Task

https://github.com/PaddlePaddle/PaddleSpeech

공식 코드

PaddleSpeech: An Easy-to-Use All-in-One Speech Toolkit

https://github.com/Pupiera/Growing_tree_on_sound

공식 코드

Growing Trees on Sounds: Assessing Strategies for End-to-End Dependency Parsing of Speech

https://github.com/ROBOTICSENGINEER/End_to_End_Learning_of_Speech_2D_Feature_Trajectory

공식 코드

End-to-End Learning of Speech 2D Feature-Trajectory for Prosthetic Hands

https://github.com/RaphaelOlivier/asr_transferability

공식 코드

Watch What You Pretrain For: Targeted, Transferable Adversarial Examples on Self-Supervised Speech Recognition models

https://github.com/RuABraun/texterrors

공식 코드

A Comparison of Methods for OOV-word Recognition on a New Public Dataset

https://github.com/SPOClab-ca/BENDR

공식 코드

BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG data

https://github.com/SherifAbdulatif/CMGAN

공식 코드

CMGAN: Conformer-Based Metric-GAN for Monaural Speech Enhancement

https://github.com/ShiningLab/POS-Tagger-for-Punctuation-Restoration

공식 코드

Incorporating External POS Tagger for Punctuation Restoration

https://github.com/Snowdar/asv-subtools

공식 코드

Towards A Unified Conformer Structure: from ASR to ASV Task

https://github.com/SoccerNet/sn-echoes

공식 코드

SoccerNet-Echoes: A Soccer Game Audio Commentary Dataset

https://github.com/StatNLP/ada4asr

공식 코드

On-the-Fly Aligned Data Augmentation for Sequence-to-Sequence ASR

https://github.com/StevenVdEeckt/CGN_CL_Adapters

공식 코드

Using Adapters to Overcome Catastrophic Forgetting in End-to-End Automatic Speech Recognition

https://github.com/TASER2023/TASER

공식 코드

ALIF: Low-Cost Adversarial Audio Attacks on Black-Box Speech Platforms using Linguistic Features

https://github.com/TParcollet/Quaternion-Recurrent-Neural-Networks

공식 코드

Quaternion Recurrent Neural Networks

https://github.com/Trikaldarshi/LASER

공식 코드

LASER: Learning by Aligning Self-supervised Representations of Speech for Improving Content-related Tasks

https://github.com/Waffle-Liu/CEA

공식 코드

Advancing Test-Time Adaptation in Wild Acoustic Test Settings

https://github.com/Waffle-Liu/STRODE

공식 코드

STRODE: Stochastic Boundary Ordinary Differential Equation

https://github.com/Waino/morfessor-emprune

공식 코드

Morfessor EM+Prune: Improved Subword Segmentation with Expectation Maximization and Pruning

https://github.com/WeifeiJin/AudioShield

공식 코드

Whispering Under the Eaves: Protecting User Privacy Against Commercial and LLM-powered Automatic Speech Recognition Systems

https://github.com/X-LANCE/SLAM-LLM

공식 코드

An Embarrassingly Simple Approach for LLM with Strong ASR Capacity

https://github.com/YiwenShaoStephen/pychain

공식 코드

PyChain: A Fully Parallelized PyTorch Implementation of LF-MMI for End-to-End ASR

https://github.com/YuanGongND/gopt

공식 코드

Transformer-Based Multi-Aspect Multi-Granularity Non-Native English Speaker Pronunciation Assessment

https://github.com/aalto-speech/fin-parl-models

공식 코드

Finnish Parliament ASR corpus - Analysis, benchmarks and statistics

https://github.com/aalto-speech/speechbrain-cl

공식 코드

Comparison and Analysis of New Curriculum Criteria for End-to-End ASR

https://github.com/adacs-project/adacs-project.github.io

공식 코드

AdaCS: Adaptive Normalization for Enhanced Code-Switching ASR

https://github.com/ahmedheakl/arazn-llm

공식 코드

ArzEn-LLM: Code-Switched Egyptian Arabic-English Translation and Speech Recognition Using LLMs

https://github.com/ahmedshah1494/speech_robust_bench

공식 코드

Speech Robust Bench: A Robustness Benchmark For Speech Recognition

https://github.com/aiola-lab/whisper-ner

공식 코드

WhisperNER: Unified Open Named Entity and Speech Recognition

https://github.com/aisaka0v0/ts-whisper

공식 코드

Extending Whisper with prompt tuning to target-speaker ASR

https://github.com/aixplain/NoRefER

공식 코드

NoRefER: a Referenceless Quality Metric for Automatic Speech Recognition via Semi-Supervised Language Model Fine-Tuning with Contrastive Learning

https://github.com/albertwy/SWRM

공식 코드

Sentiment Word Aware Multimodal Refinement for Multimodal Sentiment Analysis with ASR Errors

https://github.com/alecokas/BiLatticeRNN-Confidence

공식 코드

Confidence Estimation for Black Box Automatic Speech Recognition Systems Using Lattice Recurrent Neural Networks

https://github.com/alefiury/se-r_2022_challenge_wav2vec2

공식 코드

Domain Specific Wav2vec 2.0 Fine-tuning For The SE&R 2022 Challenge

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.