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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 1551–1575 of 3012 papers

TitleStatusHype
Multi-Channel Multi-Speaker ASR Using 3D Spatial Feature—0
Multi-Channel Multi-Speaker ASR Using Target Speaker's Solo Segment—0
Multi-channel Opus compression for far-field automatic speech recognition with a fixed bitrate budget—0
Multi-Convformer: Extending Conformer with Multiple Convolution Kernels—0
Multi-Dialect Arabic Speech Recognition—0
Multi-Encoder Learning and Stream Fusion for Transformer-Based End-to-End Automatic Speech Recognition—0
Multi-encoder multi-resolution framework for end-to-end speech recognition—0
Multi-Geometry Spatial Acoustic Modeling for Distant Speech Recognition—0
Multi-Graph Decoding for Code-Switching ASR—0
Multi-Level Modeling Units for End-to-End Mandarin Speech Recognition—0
Multilingual Contextual Adapters To Improve Custom Word Recognition In Low-resource Languages—0
Multilingual End-to-End Speech Recognition with A Single Transformer on Low-Resource Languages—0
Multilingual End-to-End Speech Translation—0
Multilingual Speech Recognition using Knowledge Transfer across Learning Processes—0
Multilingual Speech Recognition With A Single End-To-End Model—0
Multilingual Training and Cross-lingual Adaptation on CTC-based Acoustic Model—0
Multilingual Transfer Learning for Children Automatic Speech Recognition—0
Multimodal and Multiresolution Speech Recognition with Transformers—0
Multimodal Attention Merging for Improved Speech Recognition and Audio Event Classification—0
Multimodal Audio-textual Architecture for Robust Spoken Language Understanding—0
Multimodal Audio-textual Architecture for Robust Spoken Language Understanding—0
Multimodal Corpora for Silent Speech Interaction—0
Multi-Modal Data Augmentation for End-to-End ASR—0
Multimodal Depression Classification Using Articulatory Coordination Features And Hierarchical Attention Based Text Embeddings—0
Multi-modal embeddings using multi-task learning for emotion recognition—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