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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 2751–2775 of 3012 papers

TitleStatusHype
A Text Normalisation System for Non-Standard English Words—0
A Text-to-Speech Pipeline, Evaluation Methodology, and Initial Fine-Tuning Results for Child Speech Synthesis—0
A Transfer Learning Method for Speech Emotion Recognition from Automatic Speech Recognition—0
Attacks as Defenses: Designing Robust Audio CAPTCHAs Using Attacks on Automatic Speech Recognition Systems—0
Attention-based ASR with Lightweight and Dynamic Convolutions—0
Attention based end to end Speech Recognition for Voice Search in Hindi and English—0
Attention based on-device streaming speech recognition with large speech corpus—0
Attention-based Wav2Text with Feature Transfer Learning—0
Attention Enhanced Citrinet for Speech Recognition—0
Attentive Adversarial Learning for Domain-Invariant Training—0
Attentive listening system with backchanneling, response generation and flexible turn-taking—0
A two-stage transliteration approach to improve performance of a multilingual ASR—0
A two-step approach to leverage contextual data: speech recognition in air-traffic communications—0
Audio Adversarial Examples for Robust Hybrid CTC/Attention Speech Recognition—0
Audio-attention discriminative language model for ASR rescoring—0
Audio-conditioned phonemic and prosodic annotation for building text-to-speech models from unlabeled speech data—0
Audio De-identification: A New Entity Recognition Task—0
Audio De-identification - a New Entity Recognition Task—0
Audio Enhancement for Computer Audition -- An Iterative Training Paradigm Using Sample Importance—0
Audio-visual Multi-channel Integration and Recognition of Overlapped Speech—0
Audio-visual Multi-channel Recognition of Overlapped Speech—0
Audio-visual multi-channel speech separation, dereverberation and recognition—0
Audio-visual Recognition of Overlapped speech for the LRS2 dataset—0
Audio-Visual Speech Enhancement and Separation by Utilizing Multi-Modal Self-Supervised Embeddings—0
Audio-Visual Speech Recognition is Worth 32328 Voxels—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