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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 251–275 of 3012 papers

TitleStatusHype
Mamba for Streaming ASR Combined with Unimodal AggregationCode1
Predictive Speech Recognition and End-of-Utterance Detection Towards Spoken Dialog Systems—0
Alignment-Free Training for Transducer-based Multi-Talker ASR—0
AfriHuBERT: A self-supervised speech representation model for African languagesCode0
Fine-Tuning Automatic Speech Recognition for People with Parkinson's: An Effective Strategy for Enhancing Speech Technology Accessibility—0
Efficient Long-Form Speech Recognition for General Speech In-Context Learning—0
A GEN AI Framework for Medical Note Generation—0
Improving Multilingual ASR in the Wild Using Simple N-best Re-ranking—0
Deep CLAS: Deep Contextual Listen, Attend and Spell—0
Are Transformers in Pre-trained LM A Good ASR Encoder? An Empirical Study—0
MT2KD: Towards A General-Purpose Encoder for Speech, Speaker, and Audio Events—0
Weighted Cross-entropy for Low-Resource Languages in Multilingual Speech RecognitionCode0
Speech Recognition Rescoring with Large Speech-Text Foundation Models—0
Spelling Correction through Rewriting of Non-Autoregressive ASR Lattices—0
Revisiting Acoustic Features for Robust ASR—0
Bridging Speech and Text: Enhancing ASR with Pinyin-to-Character Pre-training in LLMs—0
Boosting Code-Switching ASR with Mixture of Experts Enhanced Speech-Conditioned LLM—0
Revise, Reason, and Recognize: LLM-Based Emotion Recognition via Emotion-Specific Prompts and ASR Error CorrectionCode0
MultiMed: Multilingual Medical Speech Recognition via Attention Encoder Decoder—0
Time and Tokens: Benchmarking End-to-End Speech Dysfluency Detection—0
Fast Streaming Transducer ASR Prototyping via Knowledge Distillation with Whisper—0
A Multimodal Dense Retrieval Approach for Speech-Based Open-Domain Question Answering—0
Personalized Speech Recognition for Children with Test-Time Adaptation—0
Channel-Aware Domain-Adaptive Generative Adversarial Network for Robust Speech RecognitionCode0
META-CAT: Speaker-Informed Speech Embeddings via Meta Information Concatenation for Multi-talker ASR—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