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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>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Task-Specific Audio Coding for Machines: Machine-Learned Latent Features Are Codes for That Machine
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
VisualSpeaker: Visually-Guided 3D Avatar Lip Synthesis
Speech Quality Assessment Model Based on Mixture of Experts: System-Level Performance Enhancement and Utterance-Level Challenge Analysis
A Hybrid Machine Learning Framework for Optimizing Crop Selection via Agronomic and Economic Forecasting
DeepGesture: A conversational gesture synthesis system based on emotions and semantics
First Steps Towards Voice Anonymization for Code-Switching Speech
MambAttention: Mamba with Multi-Head Attention for Generalizable Single-Channel Speech Enhancement
Lightweight Target-Speaker-Based Overlap Transcription for Practical Streaming ASR
Multimodal Representation Learning and Fusion
AUTOMATIC PRONUNCIATION MISTAKE DETECTOR PROJECT REPORT
AI-Generated Song Detection via Lyrics Transcripts
End-to-End Spoken Grammatical Error Correction
Splitformer: An improved early-exit architecture for automatic speech recognition on edge devices
OpusLM: A Family of Open Unified Speech Language Models
LM-SPT: LM-Aligned Semantic Distillation for Speech Tokenization
RapFlow-TTS: Rapid and High-Fidelity Text-to-Speech with Improved Consistency Flow Matching
State-Space Models in Efficient Whispered and Multi-dialect Speech Recognition
Breaking the Transcription Bottleneck: Fine-tuning ASR Models for Extremely Low-Resource Fieldwork Languages
InstructTTSEval: Benchmarking Complex Natural-Language Instruction Following in Text-to-Speech Systems
Automatic Speech Recognition Biases in Newcastle English: an Error Analysis
Weight Factorization and Centralization for Continual Learning in Speech Recognition
An accurate and revised version of optical character recognition-based speech synthesis using LabVIEW
Pushing the Performance of Synthetic Speech Detection with Kolmogorov-Arnold Networks and Self-Supervised Learning Models
Improving Practical Aspects of End-to-End Multi-Talker Speech Recognition for Online and Offline Scenarios
Unifying Streaming and Non-streaming Zipformer-based ASR
Thinking in Directivity: Speech Large Language Model for Multi-Talker Directional Speech Recognition
ZipVoice: Fast and High-Quality Zero-Shot Text-to-Speech with Flow Matching
Seewo's Submission to MLC-SLM: Lessons learned from Speech Reasoning Language Models
NTU Speechlab LLM-Based Multilingual ASR System for Interspeech MLC-SLM Challenge 2025
Bi-directional Context-Enhanced Speech Large Language Models for Multilingual Conversational ASR
BUT System for the MLC-SLM Challenge
From Flat to Feeling: A Feasibility and Impact Study on Dynamic Facial Emotions in AI-Generated Avatars
Qwen vs. Gemma Integration with Whisper: A Comparative Study in Multilingual SpeechLLM Systems
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.