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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 626–650 of 3012 papers

TitleStatusHype
Automatic Speech Recognition And Limited Vocabulary: A Survey—0
Automatic Speech Recognition using Advanced Deep Learning Approaches: A survey—0
An Approach to Improve Robustness of NLP Systems against ASR Errors—0
Adaptive Axonal Delays in feedforward spiking neural networks for accurate spoken word recognition—0
Automatic Speech Recognition System-Independent Word Error Rate Estimation—0
Automatic Speech Recognition on a Firefighter TETRA Broadcast Channel—0
An Application for Building a Polish Telephone Speech Corpus—0
Automatic Speech Recognition of Low-Resource Languages Based on Chukchi—0
Automatic Speech Recognition of African American English: Lexical and Contextual Effects—0
An analysis of incorporating an external language model into a sequence-to-sequence model—0
Adaptive Activation Network For Low Resource Multilingual Speech Recognition—0
A Comparative Study on Non-Autoregressive Modelings for Speech-to-Text Generation—0
Automatic speech recognition in the diagnosis of primary progressive aphasia—0
An analysis of degenerating speech due to progressive dysarthria on ASR performance—0
Automatic Speech Recognition in German: A Detailed Error Analysis—0
Automatic Speech Recognition for Uyghur through Multilingual Acoustic Modeling—0
Analyzing Utility of Visual Context in Multimodal Speech Recognition Under Noisy Conditions—0
Adapting Whisper for Regional Dialects: Enhancing Public Services for Vulnerable Populations in the United Kingdom—0
Automatic Speech Recognition for the Ika Language—0
Analyzing the Quality and Stability of a Streaming End-to-End On-Device Speech Recognizer—0
Automatic Speech Recognition for Non-Native English: Accuracy and Disfluency Handling—0
Analyzing the Performance of Automatic Speech Recognition for Ageing Voice: Does it Correlate with Dependency Level?—0
Adapting Whisper for Code-Switching through Encoding Refining and Language-Aware Decoding—0
A Comparative Study on Neural Architectures and Training Methods for Japanese Speech Recognition—0
使用字典學習法於強健性語音辨識(The Use of Dictionary Learning Approach for Robustness Speech Recognition) [In Chinese]—0
Show:102550
← PrevPage 26 of 121Next →
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