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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 1801–1850 of 3012 papers

TitleStatusHype
Robustness of end-to-end Automatic Speech Recognition Models -- A Case Study using Mozilla DeepSpeech—0
Latency-Controlled Neural Architecture Search for Streaming Speech Recognition—0
Accent Recognition with Hybrid Phonetic Features—0
Spectral modification for recognition of children’s speech undermismatched conditions—0
End-to-End Speech Recognition from Federated Acoustic ModelsCode1
Personalized Keyphrase Detection using Speaker and Environment Information—0
Semantic Data Augmentation for End-to-End Mandarin Speech Recognition—0
Head-synchronous Decoding for Transformer-based Streaming ASR—0
Multi-Task Learning for End-to-End ASR Word and Utterance Confidence with Deletion Prediction—0
Bridging the gap between streaming and non-streaming ASR systems bydistilling ensembles of CTC and RNN-T models—0
Quantization of Deep Neural Networks for Accurate Edge Computing—0
LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from SpeechCode1
On Sampling-Based Training Criteria for Neural Language Modeling—0
Discriminative Self-training for Punctuation Prediction—0
Disfluency Detection with Unlabeled Data and Small BERT Models—0
Pre-training for Spoken Language Understanding with Joint Textual and Phonetic Representation Learning—0
Scene-aware Far-field Automatic Speech Recognition—0
Accented Speech Recognition: A Survey—0
Label-Synchronous Speech-to-Text Alignment for ASR Using Forward and Backward Transformers—0
On the Impact of Word Error Rate on Acoustic-Linguistic Speech Emotion Recognition: An Update for the Deep Learning Era—0
Advanced Long-context End-to-end Speech Recognition Using Context-expanded Transformers—0
Acoustic Data-Driven Subword Modeling for End-to-End Speech Recognition—0
MIMO Self-attentive RNN Beamformer for Multi-speaker Speech Separation—0
A Method to Reveal Speaker Identity in Distributed ASR Training, and How to Counter ItCode0
Cross-domain Speech Recognition with Unsupervised Character-level Distribution MatchingCode0
Conditional independence for pretext task selection in Self-supervised speech representation learningCode0
Source and Target Bidirectional Knowledge Distillation for End-to-end Speech Translation—0
Equivalence of Segmental and Neural Transducer Modeling: A Proof of Concept—0
Bridging the Gap Between Clean Data Training and Real-World Inference for Spoken Language Understanding—0
Improved Conformer-based End-to-End Speech Recognition Using Neural Architecture Search—0
Comparing the Benefit of Synthetic Training Data for Various Automatic Speech Recognition Architectures—0
Innovative Bert-based Reranking Language Models for Speech Recognition—0
NeMo Inverse Text Normalization: From Development To ProductionCode0
A Toolbox for Construction and Analysis of Speech DatasetsCode1
Non-autoregressive Transformer-based End-to-end ASR using BERT—0
Accented Speech Recognition Inspired by Human Perception—0
On Architectures and Training for Raw Waveform Feature Extraction in ASR—0
BSTC: A Large-Scale Chinese-English Speech Translation Dataset—0
Exploring Machine Speech Chain for Domain Adaptation and Few-Shot Speaker Adaptation—0
Contextual Semi-Supervised Learning: An Approach To Leverage Air-Surveillance and Untranscribed ATC Data in ASR Systems—0
RNN Transducer Models For Spoken Language UnderstandingCode1
WNARS: WFST based Non-autoregressive Streaming End-to-End Speech Recognition—0
Speak or Chat with Me: End-to-End Spoken Language Understanding System with Flexible InputsCode1
Pushing the Limits of Non-Autoregressive Speech Recognition—0
Capturing Multi-Resolution Context by Dilated Self-Attention—0
Exploring Targeted Universal Adversarial Perturbations to End-to-end ASR Models—0
Relaxing the Conditional Independence Assumption of CTC-based ASR by Conditioning on Intermediate Predictions—0
Comparing CTC and LFMMI for out-of-domain adaptation of wav2vec 2.0 acoustic model—0
Dissecting User-Perceived Latency of On-Device E2E Speech Recognition—0
LT-LM: a novel non-autoregressive language model for single-shot lattice rescoringCode0
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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