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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 1651–1700 of 3012 papers

TitleStatusHype
The Sound of Healthcare: Improving Medical Transcription ASR Accuracy with Large Language Models—0
The State of Commercial Automatic French Legal Speech Recognition Systems and their Impact on Court Reporters et al—0
The SUMMA Platform Prototype—0
The THUEE System Description for the IARPA OpenASR21 Challenge—0
The USFD Spoken Language Translation System for IWSLT 2014—0
The WaveSurfer Automatic Speech Recognition Plugin—0
The WAW Corpus: The First Corpus of Interpreted Speeches and their Translations for English and Arabic—0
The Xiaomi Text-to-Text Simultaneous Speech Translation System for IWSLT 2022—0
The X-LANCE Technical Report for Interspeech 2024 Speech Processing Using Discrete Speech Unit Challenge—0
The ZevoMOS entry to VoiceMOS Challenge 2022—0
"This is Houston. Say again, please". The Behavox system for the Apollo-11 Fearless Steps Challenge (phase II)—0
Thoughts on the potential to compensate a hearing loss in noise—0
Three-Module Modeling For End-to-End Spoken Language Understanding Using Pre-trained DNN-HMM-Based Acoustic-Phonetic Model—0
Thutmose Tagger: Single-pass neural model for Inverse Text Normalization—0
Tigrinya Automatic Speech recognition with Morpheme based recognition units—0
Time and Tokens: Benchmarking End-to-End Speech Dysfluency Detection—0
Time-Contrastive Learning Based Deep Bottleneck Features for Text-Dependent Speaker Verification—0
Time-Domain Speech Enhancement for Robust Automatic Speech Recognition—0
Tiny-Align: Bridging Automatic Speech Recognition and Large Language Model on the Edge—0
TLT-school: a Corpus of Non Native Children Speech—0
TODM: Train Once Deploy Many Efficient Supernet-Based RNN-T Compression For On-device ASR Models—0
Token-Level Ensemble Distillation for Grapheme-to-Phoneme Conversion—0
Token-Level Serialized Output Training for Joint Streaming ASR and ST Leveraging Textual Alignments—0
TokenSplit: Using Discrete Speech Representations for Direct, Refined, and Transcript-Conditioned Speech Separation and Recognition—0
Topic Classification on Spoken Documents Using Deep Acoustic and Linguistic Features—0
Topic Identification for Speech without ASR—0
Topic Model Robustness to Automatic Speech Recognition Errors in Podcast Transcripts—0
TouchASP: Elastic Automatic Speech Perception that Everyone Can Touch—0
Toward domain-invariant speech recognition via large scale training—0
Toward Practical Automatic Speech Recognition and Post-Processing: a Call for Explainable Error Benchmark Guideline—0
Towards Accurate Text Verbalization for ASR Based on Audio Alignment—0
Towards an Automated SOAP Note: Classifying Utterances from Medical Conversations—0
Towards an Automatic Assessment of Crowdsourced Data for NLU—0
Towards an Open-Source Dutch Speech Recognition System for the Healthcare Domain—0
Towards a Unified ASR System for the Armenian Standards—0
Towards Automated Assessment of Stuttering and Stuttering Therapy—0
Towards Automated Single Channel Source Separation using Neural Networks—0
Towards Automatic Transcription of ILSE ― an Interdisciplinary Longitudinal Study of Adult Development and Aging—0
Towards better decoding and language model integration in sequence to sequence models—0
Towards Better Meta-Initialization with Task Augmentation for Kindergarten-aged Speech Recognition—0
Towards Building an Automatic Transcription System for Language Documentation: Experiences from Muyu—0
Towards Data Distillation for End-to-end Spoken Conversational Question Answering—0
Towards efficient end-to-end speech recognition with biologically-inspired neural networks—0
Towards end-2-end learning for predicting behavior codes from spoken utterances in psychotherapy conversations—0
Towards End-to-end Automatic Code-Switching Speech Recognition—0
Towards End-to-End Code-Switching Speech Recognition—0
Towards End-to-end Unsupervised Speech Recognition—0
Towards Estimating the Upper Bound of Visual-Speech Recognition: The Visual Lip-Reading Feasibility Database—0
Towards Evaluating the Robustness of Automatic Speech Recognition Systems via Audio Style Transfer—0
Towards hate speech detection in low-resource languages: Comparing ASR to acoustic word embeddings on Wolof and Swahili—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