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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 2676–2700 of 3012 papers

TitleStatusHype
Mixed-Precision Training for NLP and Speech Recognition with OpenSeq2SeqCode0
ASR-based Features for Emotion Recognition: A Transfer Learning Approach—0
Targeted Adversarial Examples for Black Box Audio SystemsCode0
Unsupervised Cross-Modal Alignment of Speech and Text Embedding Spaces—0
A Comparison of Modeling Units in Sequence-to-Sequence Speech Recognition with the Transformer on Mandarin Chinese—0
Improved ASR for Under-Resourced Languages Through Multi-Task Learning with Acoustic Landmarks—0
TED-LIUM 3: twice as much data and corpus repartition for experiments on speaker adaptationCode3
Transfer Learning from Adult to Children for Speech Recognition: Evaluation, Analysis and Recommendations—0
Boosting Noise Robustness of Acoustic Model via Deep Adversarial Training—0
Creating Lithuanian and Latvian Speech Corpora from Inaccurately Annotated Web Data—0
Evaluation of Feature-Space Speaker Adaptation for End-to-End Acoustic Models—0
Interpersonal Relationship Labels for the CALLHOME CorpusCode0
Data-Driven Pronunciation Modeling of Swiss German Dialectal Speech for Automatic Speech Recognition—0
Simulating ASR errors for training SLU systems—0
Discovering Canonical Indian English Accents: A Crowdsourcing-based Approach—0
ASR for Documenting Acutely Under-Resourced Indigenous Languages—0
Classification of Closely Related Sub-dialects of Arabic Using Support-Vector Machines—0
Parallel Corpora in Mboshi (Bantu C25, Congo-Brazzaville)—0
Speech Rate Calculations with Short Utterances: A Study from a Speech-to-Speech, Machine Translation Mediated Map Task—0
A Vietnamese Dialog Act Corpus Based on ISO 24617-2 standard—0
Design and Development of Speech Corpora for Air Traffic Control Training—0
Pronunciation Variants and ASR of Colloquial Speech: A Case Study on Czech—0
Improved Transcription and Indexing of Oral History Interviews for Digital Humanities Research—0
Open ASR for Icelandic: Resources and a Baseline System—0
The WAW Corpus: The First Corpus of Interpreted Speeches and their Translations for English and Arabic—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