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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 1926–1950 of 3012 papers

TitleStatusHype
BembaSpeech: A Speech Recognition Corpus for the Bemba LanguageCode1
Train your classifier first: Cascade Neural Networks Training from upper layers to lower layers—0
Sparsification via Compressed Sensing for Automatic Speech Recognition—0
Effects of Layer Freezing on Transferring a Speech Recognition System to Under-resourced LanguagesCode0
A bandit approach to curriculum generation for automatic speech recognition—0
Intermediate Loss Regularization for CTC-based Speech Recognition—0
Two-Stage Augmentation and Adaptive CTC Fusion for Improved Robustness of Multi-Stream End-to-End ASR—0
Multi-Task Self-Supervised Pre-Training for Music Classification—0
Confusion2vec 2.0: Enriching Ambiguous Spoken Language Representations with SubwordsCode0
Effects of Number of Filters of Convolutional Layers on Speech Recognition Model Accuracy—0
Internal Language Model Training for Domain-Adaptive End-to-End Speech Recognition—0
Speech Recognition by Simply Fine-tuning BERT—0
BCN2BRNO: ASR System Fusion for Albayzin 2020 Speech to Text Challenge—0
BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG dataCode1
Leveraging End-to-End ASR for Endangered Language Documentation: An Empirical Study on Yoloxóchitl Mixtec—0
Exploiting Beam Search Confidence for Energy-Efficient Speech Recognition—0
Streaming Models for Joint Speech Recognition and Translation—0
Arabic Speech Recognition by End-to-End, Modular Systems and HumanCode0
Efficiently Fusing Pretrained Acoustic and Linguistic Encoders for Low-resource Speech Recognition—0
Fast offline Transformer-based end-to-end automatic speech recognition for real-world applications—0
WER-BERT: Automatic WER Estimation with BERT in a Balanced Ordinal Classification Paradigm—0
An evaluation of word-level confidence estimation for end-to-end automatic speech recognition—0
Hypothesis Stitcher for End-to-End Speaker-attributed ASR on Long-form Multi-talker Recordings—0
Why Does Decentralized Training Outperform Synchronous Training In The Large Batch Setting?—0
Learning without Forgetting: Task Aware Multitask Learning for Multi-Modality Tasks—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