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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 2401–2425 of 3012 papers

TitleStatusHype
Improved Training Techniques for Online Neural Machine Translation—0
Generating Robust Audio Adversarial Examples using Iterative Proportional Clipping—0
Breaking the Data Barrier: Towards Robust Speech Translation via Adversarial Stability Training—0
Understanding Semantics from Speech Through Pre-training—0
Improving OOV Detection and Resolution with External Language Models in Acoustic-to-Word ASR—0
Espresso: A Fast End-to-end Neural Speech Recognition ToolkitCode1
Code-Switched Language Models Using Neural Based Synthetic Data from Parallel Sentences—0
Simultaneous Speech Recognition and Speaker Diarization for Monaural Dialogue Recordings with Target-Speaker Acoustic Models—0
NeMo: a toolkit for building AI applications using Neural Modules—0
An Investigation Into On-device Personalization of End-to-end Automatic Speech Recognition Models—0
Harnessing Indirect Training Data for End-to-End Automatic Speech Translation: Tricks of the Trade—0
Integrating Source-channel and Attention-based Sequence-to-sequence Models for Speech Recognition—0
A Comparative Study on Transformer vs RNN in Speech ApplicationsCode0
Large-Scale Multilingual Speech Recognition with a Streaming End-to-End Model—0
Neural Network-Based Modeling of Phonetic Durations—0
Motivations, challenges, and perspectives for the development of an Automatic Speech Recognition System for the under-resourced Ngiemboon Language—0
Semantic Language Model for Tunisian Dialect—0
Human-Informed Speakers and Interpreters Analysis in the WAW Corpus and an Automatic Method for Calculating Interpreters' D\'ecalage—0
Towards Accurate Text Verbalization for ASR Based on Audio Alignment—0
Dialect-Specific Models for Automatic Speech Recognition of African American Vernacular English—0
Deploying Technology to Save Endangered Languages—0
Gender Representation in French Broadcast Corpora and Its Impact on ASR Performance—0
Towards Better Understanding of Spontaneous Conversations: Overcoming Automatic Speech Recognition Errors With Intent Recognition—0
Two-Staged Acoustic Modeling Adaption for Robust Speech Recognition by the Example of German Oral History Interviews—0
End-to-End Multi-Speaker Speech Recognition using Speaker Embeddings and Transfer Learning—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