SOTAVerified

Automatic Speech Recognition (ASR)

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.

Papers

Showing 1–10 of 3012 papers

TitleStatusHype
NonverbalTTS: A Public English Corpus of Text-Aligned Nonverbal Vocalizations with Emotion Annotations for Text-to-Speech—0
WhisperKit: On-device Real-time ASR with Billion-Scale Transformers—0
Lightweight Target-Speaker-Based Overlap Transcription for Practical Streaming ASR—0
End-to-End Spoken Grammatical Error Correction—0
AI-Generated Song Detection via Lyrics TranscriptsCode0
LM-SPT: LM-Aligned Semantic Distillation for Speech Tokenization—0
Breaking the Transcription Bottleneck: Fine-tuning ASR Models for Extremely Low-Resource Fieldwork Languages—0
Automatic Speech Recognition Biases in Newcastle English: an Error Analysis—0
Improving Practical Aspects of End-to-End Multi-Talker Speech Recognition for Online and Offline Scenarios—0
Unifying Streaming and Non-streaming Zipformer-based ASR—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Conformer Transducer (German)WER (%)8.98—Unverified