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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 851–875 of 3012 papers

TitleStatusHype
Wiki-En-ASR-Adapt: Large-scale synthetic dataset for English ASR Customization—0
SSHR: Leveraging Self-supervised Hierarchical Representations for Multilingual Automatic Speech Recognition—0
Enabling Differentially Private Federated Learning for Speech Recognition: Benchmarks, Adaptive Optimizers and Gradient Clipping—0
Contextual Biasing with the Knuth-Morris-Pratt Matching Algorithm—0
Hierarchical Cross-Modality Knowledge Transfer with Sinkhorn Attention for CTC-based ASR—0
Segmentation-Free Streaming Machine TranslationCode0
Learning from Flawed Data: Weakly Supervised Automatic Speech Recognition—0
Segment-Level Vectorized Beam Search Based on Partially Autoregressive Inference—0
Connecting Speech Encoder and Large Language Model for ASR—0
Speech enhancement with frequency domain auto-regressive modeling—0
Cross-modal Alignment with Optimal Transport for CTC-based ASR—0
Human Transcription Quality ImprovementCode0
Massive End-to-end Models for Short Search Queries—0
Affect Recognition in Conversations Using Large Language Models—0
Dynamic ASR Pathways: An Adaptive Masking Approach Towards Efficient Pruning of A Multilingual ASR Model—0
Importance of Smoothness Induced by Optimizers in FL4ASR: Towards Understanding Federated Learning for End-to-End ASR—0
Big model only for hard audios: Sample dependent Whisper model selection for efficient inferencesCode0
Sparsely Shared LoRA on Whisper for Child Speech Recognition—0
Leveraging Data Collection and Unsupervised Learning for Code-switched Tunisian Arabic Automatic Speech Recognition—0
Semi-Autoregressive Streaming ASR With Label Context—0
Incorporating Ultrasound Tongue Images for Audio-Visual Speech Enhancement—0
Exploring Speech Enhancement for Low-resource Speech Synthesis—0
Harnessing the Zero-Shot Power of Instruction-Tuned Large Language Model in End-to-End Speech Recognition—0
Instruction-Following Speech Recognition—0
HTEC: Human Transcription Error Correction—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