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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 226–250 of 3012 papers

TitleStatusHype
Enhancing Low-Resource ASR through Versatile TTS: Bridging the Data Gap—0
DENOASR: Debiasing ASRs through Selective Denoising—0
VoiceBench: Benchmarking LLM-Based Voice AssistantsCode3
Interventional Speech Noise Injection for ASR Generalizable Spoken Language Understanding—0
Acoustic Model Optimization over Multiple Data Sources: Merging and Valuation—0
End-to-End Transformer-based Automatic Speech Recognition for Northern Kurdish: A Pioneering Approach—0
Enhancing Multimodal Sentiment Analysis for Missing Modality through Self-Distillation and Unified Modality Cross-AttentionCode1
AC-Mix: Self-Supervised Adaptation for Low-Resource Automatic Speech Recognition using Agnostic Contrastive Mixup—0
Parameter-efficient Adaptation of Multilingual Multimodal Models for Low-resource ASR—0
Failing Forward: Improving Generative Error Correction for ASR with Synthetic Data and Retrieval Augmentation—0
Roadmap towards Superhuman Speech Understanding using Large Language Models—0
Automatic Speech Recognition with BERT and CTC Transformers: A Review—0
Enhancing Indonesian Automatic Speech Recognition: Evaluating Multilingual Models with Diverse Speech Variabilities—0
A two-stage transliteration approach to improve performance of a multilingual ASR—0
Advocating Character Error Rate for Multilingual ASR Evaluation—0
Automatic Screening for Children with Speech Disorder using Automatic Speech Recognition: Opportunities and Challenges—0
CR-CTC: Consistency regularization on CTC for improved speech recognition—0
The OCON model: an old but green solution for distributable supervised classification for acoustic monitoring in smart cities—0
Algorithms For Automatic Accentuation And Transcription Of Russian Texts In Speech Recognition Systems—0
Convolutional Variational Autoencoders for Spectrogram Compression in Automatic Speech Recognition—0
Spoken Grammar Assessment Using LLM—0
End-to-End Speech Recognition with Pre-trained Masked Language Model—0
Recent Advances in Speech Language Models: A SurveyCode2
VHASR: A Multimodal Speech Recognition System With Vision HotwordsCode1
Automatic Speech Recognition for the Ika Language—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