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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 2076–2100 of 3012 papers

TitleStatusHype
End-to-End Speech Recognition and Disfluency RemovalCode1
EasyASR: A Distributed Machine Learning Platform for End-to-end Automatic Speech Recognition—0
Multi-modal embeddings using multi-task learning for emotion recognition—0
Unmanned Aerial Vehicle Control Through Domain-based Automatic Speech Recognition—0
KoSpeech: Open-Source Toolkit for End-to-End Korean Speech RecognitionCode1
Robust Spoken Language Understanding with RL-based Value Error Recovery—0
Silent Speech Interfaces for Speech Restoration: A Review—0
Voice Conversion by Cascading Automatic Speech Recognition and Text-to-Speech Synthesis with Prosody Transfer—0
Convolutional Speech Recognition with Pitch and Voice Quality Features—0
Multi-view Attention-based Speech Enhancement Model for Noise-robust Automatic Speech Recognition—0
Data augmentation using prosody and false starts to recognize non-native children's speechCode0
Learned Transferable Architectures Can Surpass Hand-Designed Architectures for Large Scale Speech Recognition—0
Aphasic Speech Recognition using a Mixture of Speech Intelligibility Experts—0
Improving Tail Performance of a Deliberation E2E ASR Model Using a Large Text Corpus—0
Cross-Utterance Language Models with Acoustic Error Sampling—0
Are Neural Open-Domain Dialog Systems Robust to Speech Recognition Errors in the Dialog History? An Empirical StudyCode0
Speech To Semantics: Improve ASR and NLU Jointly via All-Neural Interfaces—0
Sum-Product Networks for Robust Automatic Speaker IdentificationCode1
Large-scale Transfer Learning for Low-resource Spoken Language Understanding—0
MASRI-HEADSET: A Maltese Corpus for Speech Recognition—0
Conv-Transformer Transducer: Low Latency, Low Frame Rate, Streamable End-to-End Speech Recognition—0
Transfer Learning Approaches for Streaming End-to-End Speech Recognition System—0
Online Automatic Speech Recognition with Listen, Attend and Spell Model—0
Investigation of End-To-End Speaker-Attributed ASR for Continuous Multi-Talker RecordingsCode1
Transformer with Bidirectional Decoder for Speech Recognition—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