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Speech Recognition

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하위 태스크: 11
평가표: 236
보존된 평가 기록: 398

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://bitbucket.org/doglic/asr

공식 코드

Learning Waveform-Based Acoustic Models using Deep Variational Convolutional Neural Networks

https://bitbucket.org/edge-team-leat/microai_public

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Quantization and Deployment of Deep Neural Networks on Microcontrollers

https://bitbucket.org/rumschuettel/rvqe

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Recurrent Quantum Neural Networks

https://github.com/0913ktg/sc_vall-e

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SC VALL-E: Style-Controllable Zero-Shot Text to Speech Synthesizer

https://github.com/0nutation/speechgpt

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SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities

https://github.com/0xrutvij/slothspeech

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SlothSpeech: Denial-of-service Attack Against Speech Recognition Models

https://github.com/193746/VHASR

공식 코드

VHASR: A Multimodal Speech Recognition System With Vision Hotwords

https://github.com/193746/jsrsl

공식 코드

Joint Automatic Speech Recognition And Structure Learning For Better Speech Understanding

https://github.com/9yte/VenoMave

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VenoMave: Targeted Poisoning Against Speech Recognition

https://github.com/AFAgarap/gru-svm

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A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection in Network Traffic Data

https://github.com/AI4Bharat/Rasa

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Rasa: Building Expressive Speech Synthesis Systems for Indian Languages in Low-resource Settings

https://github.com/AI4Bharat/indicSUPERB

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IndicSUPERB: A Speech Processing Universal Performance Benchmark for Indian languages

https://github.com/ARM-software/keyword-transformer

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Keyword Transformer: A Self-Attention Model for Keyword Spotting

https://github.com/AdaDTur/ProGRes

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ProGRes: Prompted Generative Rescoring on ASR n-Best

https://github.com/Agrover112/Goodness-of-Pronunciation-Pipelines-for-OOV-Problem

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Goodness of Pronunciation Pipelines for OOV Problem

https://github.com/Alexander-H-Liu/End-to-end-ASR-Pytorch

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Sequence-to-sequence Automatic Speech Recognition with Word Embedding Regularization and Fused Decoding

https://github.com/AmirAbaskohi/Automatic-Speech-recognition-for-Speech-Assessment-of-Persian-Preschool-Children

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Automatic Speech Recognition for Speech Assessment of Persian Preschool Children

https://github.com/AmirmohammadRostami/KeywordsSpotting-EfficientNet-A0

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EfficientNet-Absolute Zero for Continuous Speech Keyword Spotting

https://github.com/AntonioCarta/mslmn

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Incremental Training of a Recurrent Neural Network Exploiting a Multi-Scale Dynamic Memory

https://github.com/AsrAttackSok/asr-attack-sok

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SoK: A Modularized Approach to Study the Security of Automatic Speech Recognition Systems

https://github.com/Audio-WestlakeU/CleanMel

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CleanMel: Mel-Spectrogram Enhancement for Improving Both Speech Quality and ASR

https://github.com/Audio-WestlakeU/UMA-ASR

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Unimodal Aggregation for CTC-based Speech Recognition

https://github.com/Audio-WestlakeU/VINP

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VINP: Variational Bayesian Inference with Neural Speech Prior for Joint ASR-Effective Speech Dereverberation and Blind RIR Identification

https://github.com/BME-SmartLab/DeepMOS

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Improving Self-Supervised Learning-based MOS Prediction Networks

https://github.com/BME-SmartLab/txt-ult2wav

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Speech Synthesis from Text and Ultrasound Tongue Image-based Articulatory Input

https://github.com/BME-SmartLab/txt2ult

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Extending Text-to-Speech Synthesis with Articulatory Movement Prediction using Ultrasound Tongue Imaging

https://github.com/BUTSpeechFIT/BrnoLM

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Text Augmentation for Language Models in High Error Recognition Scenario

https://github.com/BUTSpeechFIT/TS-ASR-Whisper

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DiCoW: Diarization-Conditioned Whisper for Target Speaker Automatic Speech Recognition

https://github.com/Beilong-Tang/TSELM

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TSELM: Target Speaker Extraction using Discrete Tokens and Language Models

https://github.com/Beilong-Tang/lauraTSE_code

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LauraTSE: Target Speaker Extraction using Auto-Regressive Decoder-Only Language Models

https://github.com/Berkeley-Speech-Group/PWVD-Pitch-Tracker

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A Fast and Accurate Pitch Estimation Algorithm Based on the Pseudo Wigner-Ville Distribution

https://github.com/BinhangYuan/IST_Release

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Distributed Learning of Deep Neural Networks using Independent Subnet Training

https://github.com/BogiHsu/Universal-Adaptor

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Universal Adaptor: Converting Mel-Spectrograms Between Different Configurations for Speech Synthesis

https://github.com/BriansIDP/RTLM

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Transformer Language Models with LSTM-based Cross-utterance Information Representation

https://github.com/CW-Huang/NAF

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Neural Autoregressive Flows

https://github.com/Choddeok/EmoSphere-TTS

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EmoSphere-TTS: Emotional Style and Intensity Modeling via Spherical Emotion Vector for Controllable Emotional Text-to-Speech

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