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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 1201–1250 of 3012 papers

TitleStatusHype
Exponentially Decaying Bag-of-Words Input Features for Feed-Forward Neural Network in Statistical Machine Translation—0
Extended Graph Temporal Classification for Multi-Speaker End-to-End ASR—0
Extending Recurrent Neural Aligner for Streaming End-to-End Speech Recognition in Mandarin—0
E-RNN: Design Optimization for Efficient Recurrent Neural Networks in FPGAs—0
Extracting Biomedical Entities from Noisy Audio Transcripts—0
Extracting Domain Invariant Features by Unsupervised Learning for Robust Automatic Speech Recognition—0
Extreme Encoder Output Frame Rate Reduction: Improving Computational Latencies of Large End-to-End Models—0
Face-Dubbing++: Lip-Synchronous, Voice Preserving Translation of Videos—0
Facetron: A Multi-speaker Face-to-Speech Model based on Cross-modal Latent Representations—0
Boosting Chinese ASR Error Correction with Dynamic Error Scaling Mechanism—0
Factual Consistency Oriented Speech Recognition—0
Failing Forward: Improving Generative Error Correction for ASR with Synthetic Data and Retrieval Augmentation—0
FairLENS: Assessing Fairness in Law Enforcement Speech Recognition—0
Fairness of Automatic Speech Recognition in Cleft Lip and Palate Speech—0
Falling silent, lost for words ... Tracing personal involvement in interviews with Dutch war veterans—0
Calibration of Phone Likelihoods in Automatic Speech Recognition—0
Equivalence of Segmental and Neural Transducer Modeling: A Proof of Concept—0
Fast and Accurate OOV Decoder on High-Level Features—0
Fast and Robust Unsupervised Contextual Biasing for Speech Recognition—0
Fast Context-Biasing for CTC and Transducer ASR models with CTC-based Word Spotter—0
Fast Contextual Adaptation with Neural Associative Memory for On-Device Personalized Speech Recognition—0
FastCorrect 2: Fast Error Correction on Multiple Candidates for Automatic Speech Recognition—0
FastCorrect: Fast Error Correction with Edit Alignment for Automatic Speech Recognition—0
E-PUR: An Energy-Efficient Processing Unit for Recurrent Neural Networks—0
Can We Train a Language Model Inside an End-to-End ASR Model? - Investigating Effective Implicit Language Modeling—0
Fast Entropy-Based Methods of Word-Level Confidence Estimation for End-To-End Automatic Speech Recognition—0
FastInject: Injecting Unpaired Text Data into CTC-based ASR training—0
BLSTM-Based Confidence Estimation for End-to-End Speech Recognition—0
An Investigative Study of Multi-Modal Cross-Lingual Retrieval—0
Fast Real-time Personalized Speech Enhancement: End-to-End Enhancement Network (E3Net) and Knowledge Distillation—0
Environment-aware Reconfigurable Noise Suppression—0
Entity resolution for noisy ASR transcripts—0
Blockwise Streaming Transformer for Spoken Language Understanding and Simultaneous Speech Translation—0
Fast Word Error Rate Estimation Using Self-Supervised Representations for Speech and Text—0
Entity Linking for Spoken Language—0
Ensemble knowledge distillation of self-supervised speech models—0
On Architectures and Training for Raw Waveform Feature Extraction in ASR—0
Feature selection using Fisher's ratio technique for automatic speech recognition—0
Federated Domain Adaptation for ASR with Full Self-Supervision—0
Enabling Differentially Private Federated Learning for Speech Recognition: Benchmarks, Adaptive Optimizers and Gradient Clipping—0
Federated Pruning: Improving Neural Network Efficiency with Federated Learning—0
Federated Self-Learning with Weak Supervision for Speech Recognition—0
FedNST: Federated Noisy Student Training for Automatic Speech Recognition—0
Fewer Hallucinations, More Verification: A Three-Stage LLM-Based Framework for ASR Error Correction—0
Fillers in Spoken Language Understanding: Computational and Psycholinguistic Perspectives—0
Filter and evolve: progressive pseudo label refining for semi-supervised automatic speech recognition—0
Blind Signal Dereverberation for Machine Speech Recognition—0
Findings of the 2024 Mandarin Stuttering Event Detection and Automatic Speech Recognition Challenge—0
Findings of the Shared Task on Speech Recognition for Vulnerable Individuals in Tamil—0
An Investigation on Applying Acoustic Feature Conversion to ASR of Adult and Child Speech—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