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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 1001–1025 of 3012 papers

TitleStatusHype
Effects of Speaker Count, Duration, and Accent Diversity on Zero-Shot Accent Robustness in Low-Resource ASR—0
Efficient acoustic feature transformation in mismatched environments using a Guided-GAN—0
Boosting Active Learning for Speech Recognition with Noisy Pseudo-labeled Samples—0
Acoustic-to-articulatory Speech Inversion with Multi-task Learning—0
Efficient Compression of Multitask Multilingual Speech Models—0
Continual learning using lattice-free MMI for speech recognition—0
Continual Learning in Machine Speech Chain Using Gradient Episodic Memory—0
A Survey on Speech Large Language Models—0
Continual Learning for On-Device Speech Recognition using Disentangled Conformers—0
Efficiently Train ASR Models that Memorize Less and Perform Better with Per-core Clipping—0
A Language Agnostic Multilingual Streaming On-Device ASR System—0
Efficient Sequence Training of Attention Models using Approximative Recombination—0
Efficient Utilization of Large Pre-Trained Models for Low Resource ASR—0
ELAICHI: Enhancing Low-resource TTS by Addressing Infrequent and Low-frequency Character Bigrams—0
ELITR: European Live Translator—0
El-WOZ: a client-server wizard-of-oz interface—0
Contextual-Utterance Training for Automatic Speech Recognition—0
Emotion recognition by fusing time synchronous and time asynchronous representations—0
Contextual Speech Recognition with Difficult Negative Training Examples—0
Employing Hybrid Deep Neural Networks on Dari Speech—0
Employing low-pass filtered temporal speech features for the training of ideal ratio mask in speech enhancement—0
Empowering the Deaf and Hard of Hearing Community: Enhancing Video Captions Using Large Language Models—0
A Survey of Multilingual Models for Automatic Speech Recognition—0
Contextual Semi-Supervised Learning: An Approach To Leverage Air-Surveillance and Untranscribed ATC Data in ASR Systems—0
Contextual RNN-T For Open Domain 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