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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 826–850 of 3012 papers

TitleStatusHype
Amharic-English Speech Translation in Tourism Domain—0
Data-selective Transfer Learning for Multi-Domain Speech Recognition—0
DCF-DS: Deep Cascade Fusion of Diarization and Separation for Speech Recognition under Realistic Single-Channel Conditions—0
DCTX-Conformer: Dynamic context carry-over for low latency unified streaming and non-streaming Conformer ASR—0
DECCA Repurposed: Detecting transcription inconsistencies without an orthographic standard—0
Cascaded Cross-Modal Transformer for Request and Complaint Detection—0
Decoder-only Architecture for Speech Recognition with CTC Prompts and Text Data Augmentation—0
Decoder-only Architecture for Streaming End-to-end Speech Recognition—0
Decoupled Federated Learning for ASR with Non-IID Data—0
Decoupled Structure for Improved Adaptability of End-to-End Models—0
Decoupling Pronunciation and Language for End-to-end Code-switching Automatic Speech Recognition—0
Decoupling recognition and transcription in Mandarin ASR—0
Are Transformers in Pre-trained LM A Good ASR Encoder? An Empirical Study—0
Deep CLAS: Deep Contextual Listen, Attend and Spell—0
DeepCon: An End-to-End Multilingual Toolkit for Automatic Minuting of Multi-Party Dialogues—0
Deep context: end-to-end contextual speech recognition—0
Adversarial Speaker Disentanglement Using Unannotated External Data for Self-supervised Representation Based Voice Conversion—0
Deep Graph Random Process for Relational-Thinking-Based Speech Recognition—0
Deep Learning Based Dereverberation of Temporal Envelopesfor Robust Speech Recognition—0
Deep Learning based Multi-Source Localization with Source Splitting and its Effectiveness in Multi-Talker Speech Recognition—0
A Unified Cascaded Encoder ASR Model for Dynamic Model Sizes—0
Deep Learning for Dialogue Systems—0
Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments—0
Deep Multimodal Learning for Audio-Visual Speech Recognition—0
Cascaded CNN-resBiLSTM-CTC: An End-to-End Acoustic Model For Speech 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