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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 1451–1500 of 3012 papers

TitleStatusHype
Improving low-resource ASR performance with untranscribed out-of-domain data—0
Improving Low Resource Code-switched ASR using Augmented Code-switched TTS—0
Improving Low-Resource Speech Recognition with Pretrained Speech Models: Continued Pretraining vs. Semi-Supervised Training—0
Contextual Adapters for Personalized Speech Recognition in Neural Transducers—0
Self-Supervised Learning for Multi-Channel Neural Transducer—0
In-Context Learning Boosts Speech Recognition via Human-like Adaptation to Speakers and Language Varieties—0
Enhancing Lyrics Transcription on Music Mixtures with Consistency Loss—0
Improving Massively Multilingual ASR With Auxiliary CTC Objectives—0
Improving Medical Speech-to-Text Accuracy with Vision-Language Pre-training Model—0
Improving Membership Inference in ASR Model Auditing with Perturbed Loss Features—0
Improving Multilingual ASR in the Wild Using Simple N-best Re-ranking—0
Improving Multilingual Speech Models on ML-SUPERB 2.0: Fine-tuning with Data Augmentation and LID-Aware CTC—0
Enhancing Low-Resource ASR through Versatile TTS: Bridging the Data Gap—0
Improving Named Entity Recognition in Spoken Dialog Systems by Context and Speech Pattern Modeling—0
Improving Named Entity Transcription with Contextual LLM-based Revision—0
Improving Neural Biasing for Contextual Speech Recognition by Early Context Injection and Text Perturbation—0
Improving Neural Language Models with Weight Norm Initialization and Regularization—0
Improving noise robust automatic speech recognition with single-channel time-domain enhancement network—0
Improving Noise Robustness of an End-to-End Neural Model for Automatic Speech Recognition—0
Improving noise robustness of automatic speech recognition via parallel data and teacher-student learning—0
Improving Noise Robustness of Contrastive Speech Representation Learning with Speech Reconstruction—0
Improving Noisy Student Training on Non-target Domain Data for Automatic Speech Recognition—0
Improving non-autoregressive end-to-end speech recognition with pre-trained acoustic and language models—0
Improving Practical Aspects of End-to-End Multi-Talker Speech Recognition for Online and Offline Scenarios—0
Improving Proper Noun Recognition in End-to-End ASR By Customization of the MWER Loss Criterion—0
Improving Punctuation Restoration for Speech Transcripts via External Data—0
Improving Rare Words Recognition through Homophone Extension and Unified Writing for Low-resource Cantonese Speech Recognition—0
Improving Readability for Automatic Speech Recognition Transcription—0
An Investigation of Hybrid architectures for Low Resource Multilingual Speech Recognition system in Indian context—0
Improving RNN-T ASR Performance with Date-Time and Location Awareness—0
Enhancing Large Language Model-based Speech Recognition by Contextualization for Rare and Ambiguous Words—0
Continual Learning for On-Device Speech Recognition using Disentangled Conformers—0
Improving RNN transducer with normalized jointer network—0
Improving Robustness of Neural Inverse Text Normalization via Data-Augmentation, Semi-Supervised Learning, and Post-Aligning Method—0
Improving Scheduled Sampling for Neural Transducer-based ASR—0
Enhancing Indonesian Automatic Speech Recognition: Evaluating Multilingual Models with Diverse Speech Variabilities—0
Catch Me If You Can: Blackbox Adversarial Attacks on Automatic Speech Recognition using Frequency Masking—0
Random Utterance Concatenation Based Data Augmentation for Improving Short-video Speech Recognition—0
Improving Speech-based Emotion Recognition with Contextual Utterance Analysis and LLMs—0
Improving Speech Recognition for African American English With Audio Classification—0
Artificial Intelligence for Cochlear Implants: Review of Strategies, Challenges, and Perspectives—0
Improving speech recognition models with small samples for air traffic control systems—0
Improving Speech Recognition on Noisy Speech via Speech Enhancement with Multi-Discriminators CycleGAN—0
Improving Spoken Language Understanding By Exploiting ASR N-best Hypotheses—0
Improving Streaming Automatic Speech Recognition With Non-Streaming Model Distillation On Unsupervised Data—0
Improving Streaming End-to-End ASR on Transformer-based Causal Models with Encoder States Revision Strategies—0
Improving Streaming Transformer Based ASR Under a Framework of Self-supervised Learning—0
Improving Tail Performance of a Deliberation E2E ASR Model Using a Large Text Corpus—0
Enhancing Documentation of Hupa with Automatic Speech Recognition—0
Enhancing Dialogue Speech Recognition with Robust Contextual Awareness via Noise Representation Learning—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