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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
Cascade RNN-Transducer: Syllable Based Streaming On-device Mandarin Speech Recognition with a Syllable-to-Character Converter—0
Articulatory and bottleneck features for speaker-independent ASR of dysarthric speech—0
Cascaded Models With Cyclic Feedback For Direct Speech Translation—0
FinAudio: A Benchmark for Audio Large Language Models in Financial Applications—0
Cascaded encoders for unifying streaming and non-streaming ASR—0
A review of on-device fully neural end-to-end automatic speech recognition algorithms—0
Adversarial Training for Multilingual Acoustic Modeling—0
Accented Speech Recognition: A Survey—0
Cascaded Cross-Modal Transformer for Request and Complaint Detection—0
FedNST: Federated Noisy Student Training for Automatic Speech Recognition—0
Are Transformers in Pre-trained LM A Good ASR Encoder? An Empirical Study—0
Cascaded CNN-resBiLSTM-CTC: An End-to-End Acoustic Model For Speech Recognition—0
Federated Domain Adaptation for ASR with Full Self-Supervision—0
CarneliNet: Neural Mixture Model for Automatic Speech Recognition—0
Adversarial synthesis based data-augmentation for code-switched spoken language identification—0
Capturing Multi-Resolution Context by Dilated Self-Attention—0
FeaRLESS: Feature Refinement Loss for Ensembling Self-Supervised Learning Features in Robust End-to-end Speech Recognition—0
Capitalization and Punctuation Restoration: a Survey—0
Can You Hear It? Backdoor Attacks via Ultrasonic Triggers—0
Fast Streaming Transducer ASR Prototyping via Knowledge Distillation with Whisper—0
Can Whisper perform speech-based in-context learning?—0
Are disentangled representations all you need to build speaker anonymization systems?—0
Adversarial Speaker Disentanglement Using Unannotated External Data for Self-supervised Representation Based Voice Conversion—0
Fast Real-time Personalized Speech Enhancement: End-to-End Enhancement Network (E3Net) and Knowledge Distillation—0
Fast offline Transformer-based end-to-end automatic speech recognition for real-world applications—0
FastInject: Injecting Unpaired Text Data into CTC-based ASR training—0
Can We Trust Explainable AI Methods on ASR? An Evaluation on Phoneme Recognition—0
A Recorded Debating Dataset—0
Fast Entropy-Based Methods of Word-Level Confidence Estimation for End-To-End Automatic Speech Recognition—0
Can We Train a Language Model Inside an End-to-End ASR Model? - Investigating Effective Implicit Language Modeling—0
FastCorrect: Fast Error Correction with Edit Alignment for Automatic Speech Recognition—0
Can Visual Context Improve Automatic Speech Recognition for an Embodied Agent?—0
Adversarial Speaker Adaptation—0
Fast Word Error Rate Estimation Using Self-Supervised Representations for Speech and Text—0
FastCorrect 2: Fast Error Correction on Multiple Candidates for Automatic Speech Recognition—0
Fast Contextual Adaptation with Neural Associative Memory for On-Device Personalized Speech Recognition—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
Cantonese Automatic Speech Recognition Using Transfer Learning from Mandarin—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
Fast Context-Biasing for CTC and Transducer ASR models with CTC-based Word Spotter—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
Fast and Robust Unsupervised Contextual Biasing for 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
A Wav2vec2-Based Experimental Study on Self-Supervised Learning Methods to Improve Child Speech 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