SOTAVerified

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

Showing 11261150 of 3012 papers

TitleStatusHype
Boosting Noise Robustness of Acoustic Model via Deep Adversarial Training0
BLSTM-Based Confidence Estimation for End-to-End Speech Recognition0
Estimating Phoneme Class Conditional Probabilities from Raw Speech Signal using Convolutional Neural Networks0
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 automatique0
EURO: ESPnet Unsupervised ASR Open-source Toolkit0
Euronews: a multilingual speech corpus for ASR0
Europarl-ST: A Multilingual Corpus For Speech Translation Of Parliamentary Debates0
Evaluating and Improving Automatic Speech Recognition Systems for Korean Meteorological Experts0
Evaluating and Improving Child-Directed Automatic Speech Recognition0
Evaluating and reducing the distance between synthetic and real speech distributions0
An Investigative Study of Multi-Modal Cross-Lingual Retrieval0
Evaluating Automatic Speech Recognition Systems in Comparison With Human Perception Results Using Distinctive Feature Measures0
Evaluating Automatic Speech Recognition Quality and Its Impact on Counselor Utterance Coding0
Evaluating Automatic Speech Recognition in Translation0
Evaluating Automatic Speech Recognition in an Incremental Setting0
Evaluating Low-Level Speech Features Against Human Perceptual Data0
Evaluating OpenAI's Whisper ASR for Punctuation Prediction and Topic Modeling of life histories of the Museum of the Person0
Evaluating Standard and Dialectal Frisian ASR: Multilingual Fine-tuning and Language Identification for Improved Low-resource Performance0
Evaluating User Perception of Speech Recognition System Quality with Semantic Distance Metric0
Evaluating Voice Conversion-based Privacy Protection against Informed Attackers0
Evaluating Word Embeddings for Sentence Boundary Detection in Speech Transcripts0
Evaluation of Automated Speech Recognition Systems for Conversational Speech: A Linguistic Perspective0
Evaluation of Automatic Speech Recognition for Conversational Speech in Dutch, English and German: What Goes Missing?0
Environment-aware Reconfigurable Noise Suppression0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TM-CTCTest WER10.1Unverified
2TM-seq2seqTest WER9.7Unverified
3CTC/attentionTest WER8.2Unverified
4LF-MMI TDNNTest WER6.7Unverified
5Whisper-LLaMATest WER6.6Unverified
6End2end ConformerTest WER3.9Unverified
7End2end ConformerTest WER3.7Unverified
8MoCo + wav2vec (w/o extLM)Test WER2.7Unverified
9CTC/AttentionTest WER1.5Unverified
10WhisperTest WER1.3Unverified
#ModelMetricClaimedVerifiedStatus
1SpatialNetCER14.5Unverified
2CleanMel-L-maskCER14.4Unverified
#ModelMetricClaimedVerifiedStatus
1ConformerTest WER15.32Unverified
2Whisper-largev3-finetunedTest WER10.82Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer TransducerWER (%)1.89Unverified
#ModelMetricClaimedVerifiedStatus
1DistillAVWER1.4Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer TransducerWER (%)4.28Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer TransducerWER (%)8.04Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer TransducerWER (%)3.36Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer Transducer (German)WER (%)8.98Unverified