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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 801–850 of 3012 papers

TitleStatusHype
CTC-Assisted LLM-Based Contextual ASR—0
CTC-DRO: Robust Optimization for Reducing Language Disparities in Speech Recognition—0
A review of on-device fully neural end-to-end automatic speech recognition algorithms—0
CTC Variations Through New WFST Topologies—0
Cumulative Adaptation for BLSTM Acoustic Models—0
CUNI Neural ASR with Phoneme-Level Intermediate Step for -Native at IWSLT 2020—0
Customizing Speech Recognition Model with Large Language Model Feedback—0
Cycle-consistency training for end-to-end speech recognition—0
Cycle-Consistent GAN Front-End to Improve ASR Robustness to Perturbed Speech—0
Cascaded Models With Cyclic Feedback For Direct Speech Translation—0
Cascaded encoders for unifying streaming and non-streaming ASR—0
Damage Control During Domain Adaptation for Transducer Based Automatic Speech Recognition—0
Accented Speech Recognition: A Survey—0
Deploying self-supervised learning in the wild for hybrid automatic speech recognition—0
Data Augmentation for End-to-end Code-switching Speech Recognition—0
Data Augmentation for End-to-End Speech Translation: FBK@IWSLT ‘19—0
Data Augmentation for Low-Resource Quechua ASR Improvement—0
Data Augmentation for Training Dialog Models Robust to Speech Recognition Errors—0
Data Augmentation Methods for End-to-end Speech Recognition on Distant-Talk Scenarios—0
Audio-visual multi-channel speech separation, dereverberation and recognition—0
Data Augmentation with Locally-time Reversed Speech for Automatic Speech Recognition—0
Improving Speech Emotion Recognition with Unsupervised Speaking Style Transfer—0
Deploying Technology to Save Endangered Languages—0
Data-Driven Pronunciation Modeling of Swiss German Dialectal Speech for Automatic Speech Recognition—0
Data Efficient Direct Speech-to-Text Translation with Modality Agnostic Meta-Learning—0
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