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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.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
NonverbalTTS: A Public English Corpus of Text-Aligned Nonverbal Vocalizations with Emotion Annotations for Text-to-Speech
WhisperKit: On-device Real-time ASR with Billion-Scale Transformers
Lightweight Target-Speaker-Based Overlap Transcription for Practical Streaming ASR
AI-Generated Song Detection via Lyrics Transcripts
End-to-End Spoken Grammatical Error Correction
LM-SPT: LM-Aligned Semantic Distillation for Speech Tokenization
Breaking the Transcription Bottleneck: Fine-tuning ASR Models for Extremely Low-Resource Fieldwork Languages
Automatic Speech Recognition Biases in Newcastle English: an Error Analysis
Improving Practical Aspects of End-to-End Multi-Talker Speech Recognition for Online and Offline Scenarios
Unifying Streaming and Non-streaming Zipformer-based ASR
Seewo's Submission to MLC-SLM: Lessons learned from Speech Reasoning Language Models
Bi-directional Context-Enhanced Speech Large Language Models for Multilingual Conversational ASR
BUT System for the MLC-SLM Challenge
(SimPhon Speech Test): A Data-Driven Method for In Silico Design and Validation of a Phonetically Balanced Speech Test
Lightweight and Robust Multi-Channel End-to-End Speech Recognition with Spherical Harmonic Transform
Enabling automatic transcription of child-centered audio recordings from real-world environments
Improving Named Entity Transcription with Contextual LLM-based Revision
Regularizing Learnable Feature Extraction for Automatic Speech Recognition
Addressing Pitfalls in Auditing Practices of Automatic Speech Recognition Technologies: A Case Study of People with Aphasia
Transcript-Prompted Whisper with Dictionary-Enhanced Decoding for Japanese Speech Annotation
Benchmarking Foundation Speech and Language Models for Alzheimer's Disease and Related Dementia Detection from Spontaneous Speech
Speech Recognition on TV Series with Video-guided Post-Correction
Automatic Speech Recognition of African American English: Lexical and Contextual Effects
Low-Resource Domain Adaptation for Speech LLMs via Text-Only Fine-Tuning
Bridging the Modality Gap: Softly Discretizing Audio Representation for LLM-based Automatic Speech Recognition
Lightweight Prompt Biasing for Contextualized End-to-End ASR Systems
LESS: Large Language Model Enhanced Semi-Supervised Learning for Speech Foundational Models
LLM-based phoneme-to-grapheme for phoneme-based speech recognition
Better Pseudo-labeling with Multi-ASR Fusion and Error Correction by SpeechLLM
Customizing Speech Recognition Model with Large Language Model Feedback
Effects of Speaker Count, Duration, and Accent Diversity on Zero-Shot Accent Robustness in Low-Resource ASR
Enhancing Lyrics Transcription on Music Mixtures with Consistency Loss
Overcoming Data Scarcity in Multi-Dialectal Arabic ASR via Whisper Fine-Tuning
A Multi-Dialectal Dataset for German Dialect ASR and Dialect-to-Standard Speech Translation
Unsupervised Rhythm and Voice Conversion to Improve ASR on Dysarthric Speech
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