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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 1–50 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
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
(SimPhon Speech Test): A Data-Driven Method for In Silico Design and Validation of a Phonetically Balanced Speech Test—0
Enabling automatic transcription of child-centered audio recordings from real-world environments—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
Low-Resource Domain Adaptation for Speech LLMs via Text-Only Fine-Tuning—0
Bridging the Modality Gap: Softly Discretizing Audio Representation for LLM-based Automatic Speech Recognition—0
Lightweight Prompt Biasing for Contextualized End-to-End ASR Systems—0
Customizing Speech Recognition Model with Large Language Model Feedback—0
Better Pseudo-labeling with Multi-ASR Fusion and Error Correction by SpeechLLM—0
LESS: Large Language Model Enhanced Semi-Supervised Learning for Speech Foundational Models—0
LLM-based phoneme-to-grapheme for phoneme-based speech recognition—0
Effects of Speaker Count, Duration, and Accent Diversity on Zero-Shot Accent Robustness in Low-Resource ASR—0
A Multi-Dialectal Dataset for German Dialect ASR and Dialect-to-Standard Speech Translation—0
Overcoming Data Scarcity in Multi-Dialectal Arabic ASR via Whisper Fine-Tuning—0
Enhancing Lyrics Transcription on Music Mixtures with Consistency Loss—0
HENT-SRT: Hierarchical Efficient Neural Transducer with Self-Distillation for Joint Speech Recognition and Translation—0
DNCASR: End-to-End Training for Speaker-Attributed ASR—0
Unsupervised Rhythm and Voice Conversion to Improve ASR on Dysarthric SpeechCode0
Causal Structure Discovery for Error Diagnostics of Children's ASR—0
Towards Temporally Explainable Dysarthric Speech Clarity AssessmentCode0
Vedavani: A Benchmark Corpus for ASR on Vedic Sanskrit PoetryCode0
Improving Multilingual Speech Models on ML-SUPERB 2.0: Fine-tuning with Data Augmentation and LID-Aware CTC—0
Dynamic Context-Aware Streaming Pretrained Language Model For Inverse Text Normalization—0
MSDA: Combining Pseudo-labeling and Self-Supervision for Unsupervised Domain Adaptation in ASR—0
Fewer Hallucinations, More Verification: A Three-Stage LLM-Based Framework for ASR Error Correction—0
BeaverTalk: Oregon State University's IWSLT 2025 Simultaneous Speech Translation SystemCode0
Prompting Whisper for Improved Verbatim Transcription and End-to-end Miscue Detection—0
Contextualized Automatic Speech Recognition with Dynamic Vocabulary Prediction and Activation—0
NGPU-LM: GPU-Accelerated N-Gram Language Model for Context-Biasing in Greedy ASR Decoding—0
Advancing Hearing Assessment: An ASR-Based Frequency-Specific Speech Test for Diagnosing Presbycusis—0
PSRB: A Comprehensive Benchmark for Evaluating Persian ASR Systems—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