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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 476–500 of 3012 papers

TitleStatusHype
Mai Ho'omāuna i ka 'Ai: Language Models Improve Automatic Speech Recognition in Hawaiian—0
Noise Masking Attacks and Defenses for Pretrained Speech Models—0
Houston we have a Divergence: A Subgroup Performance Analysis of ASR Models—0
LV-CTC: Non-autoregressive ASR with CTC and latent variable models—0
Multi-Stage Multi-Modal Pre-Training for Automatic Speech Recognition—0
ZAEBUC-Spoken: A Multilingual Multidialectal Arabic-English Speech Corpus—0
Extracting Biomedical Entities from Noisy Audio Transcripts—0
A Multimodal Approach to Device-Directed Speech Detection with Large Language Models—0
Isometric Neural Machine Translation using Phoneme Count Ratio Reward-based Reinforcement Learning—0
BanglaNum -- A Public Dataset for Bengali Digit Recognition from Speech—0
AdaMER-CTC: Connectionist Temporal Classification with Adaptive Maximum Entropy Regularization for Automatic Speech Recognition—0
Artificial Intelligence for Cochlear Implants: Review of Strategies, Challenges, and Perspectives—0
Automatic Speech Recognition (ASR) for the Diagnosis of pronunciation of Speech Sound Disorders in Korean children—0
SpeechColab Leaderboard: An Open-Source Platform for Automatic Speech Recognition EvaluationCode4
The evaluation of a code-switched Sepedi-English automatic speech recognition system—0
Aligning Speech to Languages to Enhance Code-switching Speech Recognition—0
Speech Robust Bench: A Robustness Benchmark For Speech RecognitionCode1
A New Benchmark for Evaluating Automatic Speech Recognition in the Arabic Call Domain—0
JEP-KD: Joint-Embedding Predictive Architecture Based Knowledge Distillation for Visual Speech Recognition—0
PixIT: Joint Training of Speaker Diarization and Speech Separation from Real-world Multi-speaker RecordingsCode2
A Cross-Modal Approach to Silent Speech with LLM-Enhanced RecognitionCode1
Automatic Speech Recognition using Advanced Deep Learning Approaches: A survey—0
Post-decoder Biasing for End-to-End Speech Recognition of Multi-turn Medical Interview—0
Inappropriate Pause Detection In Dysarthric Speech Using Large-Scale Speech Recognition—0
Probing the Information Encoded in Neural-based Acoustic Models of Automatic Speech Recognition 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