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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

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Showing 1–25 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
AI-Generated Song Detection via Lyrics TranscriptsCode0
End-to-End Spoken Grammatical Error Correction—0
Breaking the Transcription Bottleneck: Fine-tuning ASR Models for Extremely Low-Resource Fieldwork Languages—0
LM-SPT: LM-Aligned Semantic Distillation for Speech Tokenization—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
Bi-directional Context-Enhanced Speech Large Language Models for Multilingual Conversational ASR—0
BUT System for the MLC-SLM Challenge—0
Seewo's Submission to MLC-SLM: Lessons learned from Speech Reasoning Language Models—0
Lightweight and Robust Multi-Channel End-to-End Speech Recognition with Spherical Harmonic Transform—0
Enabling automatic transcription of child-centered audio recordings from real-world environments—0
(SimPhon Speech Test): A Data-Driven Method for In Silico Design and Validation of a Phonetically Balanced Speech Test—0
Improving Named Entity Transcription with Contextual LLM-based Revision—0
Regularizing Learnable Feature Extraction for Automatic Speech Recognition—0
Addressing Pitfalls in Auditing Practices of Automatic Speech Recognition Technologies: A Case Study of People with AphasiaCode0
Benchmarking Foundation Speech and Language Models for Alzheimer's Disease and Related Dementia Detection from Spontaneous Speech—0
Transcript-Prompted Whisper with Dictionary-Enhanced Decoding for Japanese Speech Annotation—0
Speech Recognition on TV Series with Video-guided Post-Correction—0
Automatic Speech Recognition of African American English: Lexical and Contextual Effects—0
Lightweight Prompt Biasing for Contextualized End-to-End ASR Systems—0
Bridging the Modality Gap: Softly Discretizing Audio Representation for LLM-based Automatic Speech Recognition—0
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All datasetsLRS2RealMANSagaleeHUI speech corpusLRS3-TEDM-AILabs speech datasetThe Spoken Wikipedia CorporaVoxforge GermanVoxPopuli

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TM-CTCTest WER10.1—Unverified
2TM-seq2seqTest WER9.7—Unverified
3CTC/attentionTest WER8.2—Unverified
4LF-MMI TDNNTest WER6.7—Unverified
5Whisper-LLaMATest WER6.6—Unverified
6End2end ConformerTest WER3.9—Unverified
7End2end ConformerTest WER3.7—Unverified
8MoCo + wav2vec (w/o extLM)Test WER2.7—Unverified
9CTC/AttentionTest WER1.5—Unverified
10WhisperTest WER1.3—Unverified
#ModelMetricClaimedVerifiedStatus
1SpatialNetCER14.5—Unverified
2CleanMel-L-maskCER14.4—Unverified
#ModelMetricClaimedVerifiedStatus
1ConformerTest WER15.32—Unverified
2Whisper-largev3-finetunedTest WER10.82—Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer TransducerWER (%)1.89—Unverified
#ModelMetricClaimedVerifiedStatus
1DistillAVWER1.4—Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer TransducerWER (%)4.28—Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer TransducerWER (%)8.04—Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer TransducerWER (%)3.36—Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer Transducer (German)WER (%)8.98—Unverified