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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 1501–1525 of 3012 papers

TitleStatusHype
Analysis of EEG frequency bands for Envisioned Speech RecognitionCode0
Shallow Fusion of Weighted Finite-State Transducer and Language Model for Text NormalizationCode0
Improving Generalization of Deep Neural Network Acoustic Models with Length Perturbation and N-best Based Label Smoothing—0
Short-Term Word-Learning in a Dynamically Changing Environment—0
Mel Frequency Spectral Domain Defenses against Adversarial Attacks on Speech Recognition Systems—0
Finnish Parliament ASR corpus - Analysis, benchmarks and statisticsCode0
A Dataset for Speech Emotion Recognition in Greek Theatrical PlaysCode0
A Speech Representation Anonymization Framework via Selective Noise PerturbationCode0
Speech-enhanced and Noise-aware Networks for Robust Speech RecognitionCode0
Impact of Dataset on Acoustic Models for Automatic Speech Recognition—0
Disentangleing Content and Fine-grained Prosody Information via Hybrid ASR Bottleneck Features for Voice Conversion—0
Lahjoita puhetta -- a large-scale corpus of spoken Finnish with some benchmarks—0
Computing Optimal Location of Microphone for Improved Speech Recognition—0
Pseudo Label Is Better Than Human Label—0
Building Robust Spoken Language Understanding by Cross Attention between Phoneme Sequence and ASR Hypothesis—0
A Text-to-Speech Pipeline, Evaluation Methodology, and Initial Fine-Tuning Results for Child Speech Synthesis—0
Exploiting Cross Domain Acoustic-to-articulatory Inverted Features For Disordered Speech Recognition—0
Representative Subset Selection for Efficient Fine-Tuning in Self-Supervised Speech Recognition—0
Prediction of speech intelligibility with DNN-based performance measures—0
Whither the Priors for (Vocal) Interactivity?—0
RED-ACE: Robust Error Detection for ASR using Confidence EmbeddingsCode0
Spectral Modification Based Data Augmentation For Improving End-to-End ASR For Children's Speech—0
Transformer-based Streaming ASR with Cumulative Attention—0
Attacks as Defenses: Designing Robust Audio CAPTCHAs Using Attacks on Automatic Speech Recognition Systems—0
A practical framework for multi-domain speech recognition and an instance sampling method to neural language modeling—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