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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 1501–1550 of 3012 papers

TitleStatusHype
An Experiment on Speech-to-Text Translation Systems for Manipuri to English on Low Resource Setting—0
Predicting lexical skills from oral reading with acoustic measures—0
Speech-T: Transducer for Text to Speech and Beyond—0
Do We Still Need Automatic Speech Recognition for Spoken Language Understanding?—0
Effect of noise suppression losses on speech distortion and ASR performance—0
Multi-Channel Multi-Speaker ASR Using 3D Spatial Feature—0
Capitalization and Punctuation Restoration: a Survey—0
Deep Spoken Keyword Spotting: An Overview—0
Switching Independent Vector Analysis and Its Extension to Blind and Spatially Guided Convolutional Beamforming Algorithms—0
SLUE: New Benchmark Tasks for Spoken Language Understanding Evaluation on Natural SpeechCode1
Lattention: Lattice-attention in ASR rescoring—0
Towards Measuring Fairness in Speech Recognition: Casual Conversations Dataset Transcriptions—0
A Conformer-based ASR Frontend for Joint Acoustic Echo Cancellation, Speech Enhancement and Speech Separation—0
Progressive Down-Sampling for Acoustic Encoding—0
Two Front-Ends, One Model : Fusing Heterogeneous Speech Features for Low Resource ASR with Multilingual Pre-Training—0
A Novel End-to-End CAPT System for L2 Children Learners—0
Heterogeneous Language Model Optimization in Automatic Speech Recognition—0
Who Are We Talking About? Handling Person Names in Speech Translation—0
Improving Multimodal Speech Recognition by Data Augmentation and Speech Representations—0
Speech-to-SQL Parsing: Error Correction with Multi-modal Representations—0
On Spoken Language Understanding Systems for Low Resourced Languages—0
Attention-based Multi-hypothesis Fusion for Speech SummarizationCode0
Attention based end to end Speech Recognition for Voice Search in Hindi and English—0
Prediction of Listener Perception of Argumentative Speech in a Crowdsourced Dataset Using (Psycho-)Linguistic and Fluency Features—0
Self-Normalized Importance Sampling for Neural Language Modeling—0
Scaling ASR Improves Zero and Few Shot Learning—0
Privacy attacks for automatic speech recognition acoustic models in a federated learning framework—0
Sequential Randomized Smoothing for Adversarially Robust Speech RecognitionCode0
Context-Aware Transformer Transducer for Speech Recognition—0
Effective Cross-Utterance Language Modeling for Conversational Speech Recognition—0
Conformer-based Hybrid ASR System for Switchboard Dataset—0
MT3: Multi-Task Multitrack Music TranscriptionCode1
A Fine-tuned Wav2vec 2.0/HuBERT Benchmark For Speech Emotion Recognition, Speaker Verification and Spoken Language Understanding—0
Speech recognition for air traffic control via feature learning and end-to-end training—0
STC speaker recognition systems for the NIST SRE 2021—0
Recent Advances in End-to-End Automatic Speech Recognition—0
Indic Languages Automatic Speech Recognition using Meta-Learning Approach—0
Speech Technology for Everyone: Automatic Speech Recognition for Non-Native English—0
Intrinsic evaluation of language models for code-switchingCode0
Collaborative Data Relabeling for Robust and Diverse Voice Apps Recommendation in Intelligent Personal Assistants—0
Voice Query Auto Completion—0
Cross Attention Augmented Transducer Networks for Simultaneous TranslationCode1
Comprehensive Punctuation Restoration for English and Polish—0
A transfer learning based approach for pronunciation scoringCode1
Sequence Transduction with Graph-based Supervision—0
SNRi Target Training for Joint Speech Enhancement and Recognition—0
Revealing and Protecting Labels in Distributed TrainingCode0
Cross-attention conformer for context modeling in speech enhancement for ASR—0
Speaker conditioning of acoustic models using affine transformation for multi-speaker speech recognition—0
Fusing ASR Outputs in Joint Training for Speech Emotion Recognition—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