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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 2426–2450 of 3012 papers

TitleStatusHype
Unsupervised Stemming based Language Model for Telugu Broadcast News Transcription—0
Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling—0
Exploiting semi-supervised training through a dropout regularization in end-to-end speech recognition—0
Mitigating Noisy Inputs for Question Answering—0
Fast and Accurate Capitalization and Punctuation for Automatic Speech Recognition Using Transformer and Chunk Merging—0
An End-to-End Text-independent Speaker Verification Framework with a Keyword Adversarial Network—0
Practical Speech Recognition with HTK—0
Imperio: Robust Over-the-Air Adversarial Examples for Automatic Speech Recognition Systems—0
V2S attack: building DNN-based voice conversion from automatic speaker verification—0
A Speech Test Set of Practice Business Presentations with Additional Relevant Texts—0
Personalizing ASR for Dysarthric and Accented Speech with Limited DataCode0
DuTongChuan: Context-aware Translation Model for Simultaneous Interpreting—0
MaSS: A Large and Clean Multilingual Corpus of Sentence-aligned Spoken Utterances Extracted from the BibleCode0
Correlation Distance Skip Connection Denoising Autoencoder (CDSK-DAE) for Speech Feature Enhancement—0
Hierarchical Sequence to Sequence Voice Conversion with Limited Data—0
Investigating Target Set Reduction for End-to-End Speech Recognition of Hindi-English Code-Switching Data—0
Large-Scale Mixed-Bandwidth Deep Neural Network Acoustic Modeling for Automatic Speech Recognition—0
Acoustic Model Optimization Based On Evolutionary Stochastic Gradient Descent with Anchors for Automatic Speech Recognition—0
A Highly Efficient Distributed Deep Learning System For Automatic Speech Recognition—0
Joint Speech Recognition and Speaker Diarization via Sequence Transduction—0
Teach an all-rounder with experts in different domains—0
Analyzing Phonetic and Graphemic Representations in End-to-End Automatic Speech RecognitionCode0
ShrinkML: End-to-End ASR Model Compression Using Reinforcement Learning—0
Improved low-resource Somali speech recognition by semi-supervised acoustic and language model training—0
End-to-End Speech Recognition with High-Frame-Rate Features Extraction—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