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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 2001–2050 of 3012 papers

TitleStatusHype
ESPnet-se: end-to-end speech enhancement and separation toolkit designed for asr integration—0
Acoustics Based Intent Recognition Using Discovered Phonetic Units for Low Resource Languages—0
Improving RNN Transducer Based ASR with Auxiliary TasksCode1
Alignment Restricted Streaming Recurrent Neural Network Transducer—0
Augmenting Images for ASR and TTS through Single-loop and Dual-loop Multimodal Chain Framework—0
Incremental Machine Speech Chain Towards Enabling Listening while Speaking in Real-time—0
Data Augmentation for End-to-end Code-switching Speech Recognition—0
Sequence-to-Sequence Learning via Attention Transfer for Incremental Speech Recognition—0
Improving RNN transducer with normalized jointer network—0
Streaming Attention-Based Models with Augmented Memory for End-to-End Speech Recognition—0
Internal Language Model Estimation for Domain-Adaptive End-to-End Speech Recognition—0
Integration of speech separation, diarization, and recognition for multi-speaker meetings: System description, comparison, and analysis—0
Unsupervised Pattern Discovery from Thematic Speech Archives Based on Multilingual Bottleneck Features—0
Minimum Bayes Risk Training for End-to-End Speaker-Attributed ASRCode1
Warped Language Models for Noise Robust Language Understanding—0
SapAugment: Learning A Sample Adaptive Policy for Data Augmentation—0
Dual-decoder Transformer for Joint Automatic Speech Recognition and Multilingual Speech TranslationCode1
DNN-Based Semantic Model for Rescoring N-best Speech Recognition List—0
ELITR: European Live Translator—0
Punctuation Restoration using Transformer Models for High-and Low-Resource LanguagesCode1
Impact of ASR on Alzheimer’s Disease Detection: All Errors are Equal, but Deletions are More Equal than Others—0
Improving End-to-End Bangla Speech Recognition with Semi-supervised Training—0
Direct Segmentation Models for Streaming Speech TranslationCode0
Effectively pretraining a speech translation decoder with Machine Translation data—0
Multilingual Bottleneck Features for Improving ASR Performance of Code-Switched Speech in Under-Resourced LanguagesCode0
Streaming Simultaneous Speech Translation with Augmented Memory Transformer—0
Joint Masked CPC and CTC Training for ASRCode1
Directional ASR: A New Paradigm for E2E Multi-Speaker Speech Recognition with Source Localization—0
Semi-Supervised Speech Recognition via Graph-based Temporal Classification—0
INT8 Winograd Acceleration for Conv1D Equipped ASR Models Deployed on Mobile Devices—0
Non-Autoregressive Transformer ASR with CTC-Enhanced Decoder Input—0
Fusion Models for Improved Visual Captioning—0
Decoupling Pronunciation and Language for End-to-end Code-switching Automatic Speech Recognition—0
Effective Decoder Masking for Transformer Based End-to-End Speech Recognition—0
Cascaded encoders for unifying streaming and non-streaming ASR—0
Emotion recognition by fusing time synchronous and time asynchronous representations—0
Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech RecognitionCode1
Improved Mask-CTC for Non-Autoregressive End-to-End ASR—0
Large-Scale End-to-End Multilingual Speech Recognition and Language Identification with Multi-Task LearningCode0
Two-stage Textual Knowledge Distillation for End-to-End Spoken Language UnderstandingCode0
Improving Noise Robustness of an End-to-End Neural Model for Automatic Speech Recognition—0
MAM: Masked Acoustic Modeling for End-to-End Speech-to-Text Translation—0
Confidence Estimation for Attention-based Sequence-to-sequence Models for Speech RecognitionCode1
Rethinking Evaluation in ASR: Are Our Models Robust Enough?Code0
Improving Streaming Automatic Speech Recognition With Non-Streaming Model Distillation On Unsupervised Data—0
How Phonotactics Affect Multilingual and Zero-shot ASR PerformanceCode0
SlimIPL: Language-Model-Free Iterative Pseudo-Labeling—0
A General Multi-Task Learning Framework to Leverage Text Data for Speech to Text Tasks—0
Knowledge Distillation for Improved Accuracy in Spoken Question Answering—0
Sentence Boundary Augmentation For Neural Machine Translation Robustness—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