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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 1051–1100 of 3012 papers

TitleStatusHype
Security and Privacy Problems in Voice Assistant Applications: A Survey—0
Political corpus creation through automatic speech recognition on EU debatesCode0
Multimodal Short Video Rumor Detection System Based on Contrastive Learning—0
A Virtual Simulation-Pilot Agent for Training of Air Traffic Controllers—0
A CTC Alignment-based Non-autoregressive Transformer for End-to-end Automatic Speech Recognition—0
Speech Reconstruction from Silent Tongue and Lip Articulation By Pseudo Target Generation and Domain Adversarial Training—0
Wav2code: Restore Clean Speech Representations via Codebook Lookup for Noise-Robust ASR—0
Scalable and Accurate Self-supervised Multimodal Representation Learning without Aligned Video and Text Data—0
Self-Supervised Learning-Based Source Separation for Meeting Data—0
The Edinburgh International Accents of English Corpus: Towards the Democratization of English ASR—0
Improving the previous state-of-the-art Frisian ASR by fine-tuning XLS-R—0
PROCTER: PROnunciation-aware ConTextual adaptER for personalized speech recognition in neural transducers—0
Joint unsupervised and supervised learning for context-aware language identification—0
Text is All You Need: Personalizing ASR Models using Controllable Speech Synthesis—0
Enhancing Unsupervised Speech Recognition with Diffusion GANs—0
Beyond Universal Transformer: block reusing with adaptor in Transformer for automatic speech recognition—0
Code-Switching Text Generation and Injection in Mandarin-English ASR—0
Knowledge Distillation from Multiple Foundation Models for End-to-End Speech Recognition—0
A Deep Learning System for Domain-specific Speech Recognition—0
DistillW2V2: A Small and Streaming Wav2vec 2.0 Based ASR Model—0
Visual Information Matters for ASR Error Correction—0
HYBRIDFORMER: improving SqueezeFormer with hybrid attention and NSR mechanismCode0
Cascading and Direct Approaches to Unsupervised Constituency Parsing on Spoken SentencesCode0
Improving Accented Speech Recognition with Multi-Domain Training—0
Fine-tuning Strategies for Faster Inference using Speech Self-Supervised Models: A Comparative StudyCode0
Transcription free filler word detection with Neural semi-CRFsCode0
MIXPGD: Hybrid Adversarial Training for Speech Recognition Systems—0
Clinical BERTScore: An Improved Measure of Automatic Speech Recognition Performance in Clinical Settings—0
wav2vec and its current potential to Automatic Speech Recognition in German for the usage in Digital History: A comparative assessment of available ASR-technologies for the use in cultural heritage contexts—0
End-to-End Speech Recognition: A Survey—0
Leveraging Large Text Corpora for End-to-End Speech Summarization—0
Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages—0
N-best T5: Robust ASR Error Correction using Multiple Input Hypotheses and Constrained Decoding Space—0
Leveraging Redundancy in Multiple Audio Signals for Far-Field Speech Recognition—0
Language-Universal Adapter Learning with Knowledge Distillation for End-to-End Multilingual Speech RecognitionCode0
Diacritic Recognition Performance in Arabic ASR—0
Improving Medical Speech-to-Text Accuracy with Vision-Language Pre-training Model—0
Deep Visual Forced Alignment: Learning to Align Transcription with Talking Face Video—0
Text-only domain adaptation for end-to-end ASR using integrated text-to-mel-spectrogram generator—0
A low latency attention module for streaming self-supervised speech representation learningCode0
Multimodal Speech Recognition for Language-Guided Embodied AgentsCode0
A Comparison of Speech Data Augmentation Methods Using S3PRL Toolkit—0
MoLE : Mixture of Language Experts for Multi-Lingual Automatic Speech Recognition—0
Efficient Ensemble for Multimodal Punctuation Restoration using Time-Delay Neural NetworkCode0
Speech Corpora Divergence Based Unsupervised Data Selection for ASR—0
Ensemble knowledge distillation of self-supervised speech models—0
Improving Massively Multilingual ASR With Auxiliary CTC Objectives—0
Factual Consistency Oriented Speech Recognition—0
Evaluating Automatic Speech Recognition in an Incremental Setting—0
MADI: Inter-domain Matching and Intra-domain Discrimination for Cross-domain 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