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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 1676–1700 of 3012 papers

TitleStatusHype
A Study of Multilingual End-to-End Speech Recognition for Kazakh, Russian, and EnglishCode1
Amortized Neural Networks for Low-Latency Speech Recognition—0
Automatic recognition of suprasegmentals in speech—0
Decoupling recognition and transcription in Mandarin ASR—0
Interactive Reinforcement Learning for Table Balancing Robot—0
ZJU’s IWSLT 2021 Speech Translation System—0
IMS’ Systems for the IWSLT 2021 Low-Resource Speech Translation Task—0
On Knowledge Distillation for Translating Erroneous Speech Transcriptions—0
ON-TRAC’ systems for the IWSLT 2021 low-resource speech translation and multilingual speech translation shared tasks—0
Without Further Ado: Direct and Simultaneous Speech Translation by AppTek in 2021—0
How Might We Create Better Benchmarks for Speech Recognition?—0
Technology-Augmented Multilingual Communication Models: New Interaction Paradigms, Shifts in the Language Services Industry, and Implications for Training Programs—0
BTS: Back TranScription for Speech-to-Text Post-Processor using Text-to-Speech-to-Text—0
QASR: QCRI Aljazeera Speech Resource A Large Scale Annotated Arabic Speech Corpus—0
The History of Speech Recognition to the Year 2030Code1
Can You Hear It? Backdoor Attacks via Ultrasonic Triggers—0
USC: An Open-Source Uzbek Speech Corpus and Initial Speech Recognition ExperimentsCode1
Adapting GPT, GPT-2 and BERT Language Models for Speech Recognition—0
An Adapter Based Pre-Training for Efficient and Scalable Self-Supervised Speech Representation Learning—0
Facetron: A Multi-speaker Face-to-Speech Model based on Cross-modal Latent Representations—0
Brazilian Portuguese Speech Recognition Using Wav2vec 2.0Code1
OLR 2021 Challenge: Datasets, Rules and Baselines—0
CarneliNet: Neural Mixture Model for Automatic Speech Recognition—0
Multitask-Based Joint Learning Approach To Robust ASR For Radio Communication Speech—0
Streaming End-to-End ASR based on Blockwise Non-Autoregressive Models—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