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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 1251–1300 of 3012 papers

TitleStatusHype
Clinical Dialogue Transcription Error Correction using Seq2Seq Models—0
Joint Training of Speech Enhancement and Self-supervised Model for Noise-robust ASR—0
Heterogeneous Reservoir Computing Models for Persian Speech Recognition—0
Investigating Lexical Replacements for Arabic-English Code-Switched Data Augmentation—0
On Building Spoken Language Understanding Systems for Low Resourced Languages—0
FLEURS: Few-shot Learning Evaluation of Universal Representations of SpeechCode0
Improving CTC-based ASR Models with Gated Interlayer Collaboration—0
An Investigation on Applying Acoustic Feature Conversion to ASR of Adult and Child Speech—0
Multi-Level Modeling Units for End-to-End Mandarin Speech Recognition—0
Calibrate and Refine! A Novel and Agile Framework for ASR-error Robust Intent Detection—0
Language Models with Image Descriptors are Strong Few-Shot Video-Language LearnersCode1
Self-Supervised Speech Representation Learning: A Review—0
PaddleSpeech: An Easy-to-Use All-in-One Speech ToolkitCode6
Automatic Spoken Language Identification using a Time-Delay Neural Network—0
Insights on Neural Representations for End-to-End Speech Recognition—0
Deploying self-supervised learning in the wild for hybrid automatic speech recognition—0
Streaming Noise Context Aware Enhancement For Automatic Speech Recognition in Multi-Talker Environments—0
Improved Consistency Training for Semi-Supervised Sequence-to-Sequence ASR via Speech Chain Reconstruction and Self-Transcribing—0
Pretraining Approaches for Spoken Language Recognition: TalTech Submission to the OLR 2021 Challenge—0
Who Are We Talking About? Handling Person Names in Speech Translation—0
Personalized Adversarial Data Augmentation for Dysarthric and Elderly Speech Recognition—0
Unified Modeling of Multi-Domain Multi-Device ASR Systems—0
A Closer Look at Audio-Visual Multi-Person Speech Recognition and Active Speaker Selection—0
End-to-End Multi-Person Audio/Visual Automatic Speech Recognition—0
Best of Both Worlds: Multi-task Audio-Visual Automatic Speech Recognition and Active Speaker Detection—0
Speaker Reinforcement Using Target Source Extraction for Robust Automatic Speech Recognition—0
Vietnamese Automatic Speech Recognition using Wav2vec 2.0Code1
Transformer-Based Multi-Aspect Multi-Granularity Non-Native English Speaker Pronunciation AssessmentCode1
A Conformer-based Waveform-domain Neural Acoustic Echo Canceller Optimized for ASR Accuracy—0
Speaker Recognition in the WildCode1
ON-TRAC Consortium Systems for the IWSLT 2022 Dialect and Low-resource Speech Translation Tasks—0
A Meeting Transcription System for an Ad-Hoc Acoustic Sensor Network—0
Wav2Seq: Pre-training Speech-to-Text Encoder-Decoder Models Using Pseudo LanguagesCode1
Discourse on ASR Measurement: Introducing the ARPOCA Assessment Tool—0
How You Say It Matters: Measuring the Impact of Verbal Disfluency Tags on Automated Dementia DetectionCode0
Automatic Speech Recognition and Query By Example for Creole Languages DocumentationCode0
SUH_ASR@LT-EDI-ACL2022: Transformer based Approach for Speech Recognition for Vulnerable Individuals in Tamil—0
The HW-TSC’s Offline Speech Translation System for IWSLT 2022 Evaluation—0
The Xiaomi Text-to-Text Simultaneous Speech Translation System for IWSLT 2022—0
JHU IWSLT 2022 Dialect Speech Translation System Description—0
Fine-tuning pre-trained models for Automatic Speech Recognition, experiments on a fieldwork corpus of Japhug (Trans-Himalayan family)—0
Findings of the Shared Task on Speech Recognition for Vulnerable Individuals in Tamil—0
SSNCSE_NLP@LT-EDI-ACL2022: Speech Recognition for Vulnerable Individuals in Tamil using pre-trained XLSR models—0
Phoneme transcription of endangered languages: an evaluation of recent ASR architectures in the single speaker scenario—0
NVIDIA NeMo Offline Speech Translation Systems for IWSLT 2022—0
MTL-SLT: Multi-Task Learning for Spoken Language Tasks—0
Enhancing Documentation of Hupa with Automatic Speech Recognition—0
Bilingual End-to-End ASR with Byte-Level Subwords—0
Mask scalar prediction for improving robust automatic speech recognition—0
Cleanformer: A multichannel array configuration-invariant neural enhancement frontend for ASR in smart speakers—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