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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 1051–1075 of 3012 papers

TitleStatusHype
Building a Unified Code-Switching ASR System for South African Languages—0
Building a Public Domain Voice Database for Odia—0
A practical framework for multi-domain speech recognition and an instance sampling method to neural language modeling—0
Building a Non-native Speech Corpus Featuring Chinese-English Bilingual Children: Compilation and Rationale—0
Building a Noisy Audio Dataset to Evaluate Machine Learning Approaches for Automatic Speech Recognition Systems—0
ApproBiVT: Lead ASR Models to Generalize Better Using Approximated Bias-Variance Tradeoff Guided Early Stopping and Checkpoint Averaging—0
Adversarial Attacks and Defenses for Speech Recognition Systems—0
A Conformer Based Acoustic Model for Robust Automatic Speech Recognition—0
1SPU: 1-step Speech Processing Unit—0
Building and Evaluation of a Real Room Impulse Response Dataset—0
Building an ASR Error Robust Spoken Virtual Patient System in a Highly Class-Imbalanced Scenario Without Speech Data—0
Applying LLMs for Rescoring N-best ASR Hypotheses of Casual Conversations: Effects of Domain Adaptation and Context Carry-over—0
Building a great multi-lingual teacher with sparsely-gated mixture of experts for speech recognition—0
Building Accurate Low Latency ASR for Streaming Voice Search—0
Aphasic Speech Recognition using a Mixture of Speech Intelligibility Experts—0
BUCEADOR, a multi-language search engine for digital libraries—0
BTS: Back TranScription for Speech-to-Text Post-Processor using Text-to-Speech-to-Text—0
BSTC: A Large-Scale Chinese-English Speech Translation Dataset—0
BS-PLCNet: Band-split Packet Loss Concealment Network with Multi-task Learning Framework and Multi-discriminators—0
A Parameterized and Annotated Spoken Dialog Corpus of the CMU Let's Go Bus Information System—0
Advancing Speech Recognition With No Speech Or With Noisy Speech—0
A Configurable Multilingual Model is All You Need to Recognize All Languages—0
Bring the Noise: Introducing Noise Robustness to Pretrained Automatic Speech Recognition—0
A Parallel Recurrent Neural Network for Language Modeling with POS Tags—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