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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 1101–1150 of 3012 papers

TitleStatusHype
An Online Attention-based Model for Speech Recognition—0
Boosting Punctuation Restoration with Data Generation and Reinforcement Learning—0
Boosting Norwegian Automatic Speech Recognition—0
A non-expert Kaldi recipe for Vietnamese Speech Recognition System—0
Advancing CTC-CRF Based End-to-End Speech Recognition with Wordpieces and Conformers—0
A Noise-Robust Self-supervised Pre-training Model Based Speech Representation Learning for Automatic Speech Recognition—0
Boosting End-to-End Multilingual Phoneme Recognition through Exploiting Universal Speech Attributes Constraints—0
A Bilingual Interactive Human Avatar Dialogue System—0
Annotated Speech Corpus for Low Resource Indian Languages: Awadhi, Bhojpuri, Braj and Magahi—0
Boosting Code-Switching ASR with Mixture of Experts Enhanced Speech-Conditioned LLM—0
Advancing Arabic Speech Recognition Through Large-Scale Weakly Supervised Learning—0
Improving sequence-to-sequence speech recognition training with on-the-fly data augmentation—0
Exploiting Sentence and Context Representations in Deep Neural Models for Spoken Language Understanding—0
Exploiting Spectral Augmentation for Code-Switched Spoken Language Identification—0
Error Correction by Paying Attention to Both Acoustic and Confidence References for Automatic Speech Recognition—0
E-RNN: Design Optimization for Efficient Recurrent Neural Networks in FPGAs—0
Boosting Chinese ASR Error Correction with Dynamic Error Scaling Mechanism—0
Equivalence of Segmental and Neural Transducer Modeling: A Proof of Concept—0
Error Correction in ASR using Sequence-to-Sequence Models—0
Error Detection in Automatic Speech Recognition—0
E-PUR: An Energy-Efficient Processing Unit for Recurrent Neural Networks—0
ESPnet: End-to-End Speech Processing Toolkit—0
ESPnet-se: end-to-end speech enhancement and separation toolkit designed for asr integration—0
ESPnet-SE++: Speech Enhancement for Robust Speech Recognition, Translation, and Understanding—0
ESPnet-ST: All-in-One Speech Translation Toolkit—0
Boosting Noise Robustness of Acoustic Model via Deep Adversarial Training—0
BLSTM-Based Confidence Estimation for End-to-End Speech Recognition—0
Estimating Phoneme Class Conditional Probabilities from Raw Speech Signal using Convolutional Neural Networks—0
Etude de la performance des modèles acoustiques pour des voix de personnes âgées en vue de l'adaptation des systèmes de RAP (Assessment of the acoustic models performance in the ageing voice case for ASR system adaptation) [in French]—0
Étude de l'informativité des transcriptions : une approche basée sur le résumé automatique—0
EURO: ESPnet Unsupervised ASR Open-source Toolkit—0
Euronews: a multilingual speech corpus for ASR—0
Europarl-ST: A Multilingual Corpus For Speech Translation Of Parliamentary Debates—0
Evaluating and Improving Automatic Speech Recognition Systems for Korean Meteorological Experts—0
Evaluating and Improving Child-Directed Automatic Speech Recognition—0
Evaluating and reducing the distance between synthetic and real speech distributions—0
An Investigative Study of Multi-Modal Cross-Lingual Retrieval—0
Evaluating Automatic Speech Recognition Systems in Comparison With Human Perception Results Using Distinctive Feature Measures—0
Evaluating Automatic Speech Recognition Quality and Its Impact on Counselor Utterance Coding—0
Evaluating Automatic Speech Recognition in Translation—0
Evaluating Automatic Speech Recognition in an Incremental Setting—0
Evaluating Low-Level Speech Features Against Human Perceptual Data—0
Evaluating OpenAI's Whisper ASR for Punctuation Prediction and Topic Modeling of life histories of the Museum of the Person—0
Evaluating Standard and Dialectal Frisian ASR: Multilingual Fine-tuning and Language Identification for Improved Low-resource Performance—0
Evaluating User Perception of Speech Recognition System Quality with Semantic Distance Metric—0
Evaluating Voice Conversion-based Privacy Protection against Informed Attackers—0
Evaluating Word Embeddings for Sentence Boundary Detection in Speech Transcripts—0
Evaluation of Automated Speech Recognition Systems for Conversational Speech: A Linguistic Perspective—0
Evaluation of Automatic Speech Recognition for Conversational Speech in Dutch, English and German: What Goes Missing?—0
Environment-aware Reconfigurable Noise Suppression—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