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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 2201–2250 of 3012 papers

TitleStatusHype
Towards an Automated SOAP Note: Classifying Utterances from Medical Conversations—0
Towards an Automatic Assessment of Crowdsourced Data for NLU—0
Towards an Open-Source Dutch Speech Recognition System for the Healthcare Domain—0
Towards a Unified ASR System for the Armenian Standards—0
Towards Automated Assessment of Stuttering and Stuttering Therapy—0
Towards Automated Single Channel Source Separation using Neural Networks—0
Towards Automatic Transcription of ILSE ― an Interdisciplinary Longitudinal Study of Adult Development and Aging—0
Towards better decoding and language model integration in sequence to sequence models—0
Towards Better Meta-Initialization with Task Augmentation for Kindergarten-aged Speech Recognition—0
Towards Building an Automatic Transcription System for Language Documentation: Experiences from Muyu—0
Towards Data Distillation for End-to-end Spoken Conversational Question Answering—0
Towards efficient end-to-end speech recognition with biologically-inspired neural networks—0
Towards end-2-end learning for predicting behavior codes from spoken utterances in psychotherapy conversations—0
Towards End-to-end Automatic Code-Switching Speech Recognition—0
Towards End-to-End Code-Switching Speech Recognition—0
Towards End-to-end Unsupervised Speech Recognition—0
Towards Estimating the Upper Bound of Visual-Speech Recognition: The Visual Lip-Reading Feasibility Database—0
Towards Evaluating the Robustness of Automatic Speech Recognition Systems via Audio Style Transfer—0
Towards hate speech detection in low-resource languages: Comparing ASR to acoustic word embeddings on Wolof and Swahili—0
Towards High-fidelity Singing Voice Conversion with Acoustic Reference and Contrastive Predictive Coding—0
Towards Lifelong Learning of End-to-end ASR—0
Towards Maximum Likelihood Training for Transducer-based Streaming Speech Recognition—0
Towards Measuring Fairness in Speech Recognition: Casual Conversations Dataset Transcriptions—0
Continuous Silent Speech Recognition using EEG—0
Towards One Model to Rule All: Multilingual Strategy for Dialectal Code-Switching Arabic ASR—0
Towards Online End-to-end Transformer Automatic Speech Recognition—0
Towards Pretraining Robust ASR Foundation Model with Acoustic-Aware Data Augmentation—0
Towards Probing Contact Center Large Language Models—0
Towards Processing of the Oral History Interviews and Related Printed Documents—0
Towards Quantum Language Models—0
Representative Subset Selection for Efficient Fine-Tuning in Self-Supervised Speech Recognition—0
Towards Selection of Text-to-speech Data to Augment ASR Training—0
Towards speech-to-text translation without speech recognition—0
Towards Stream Translation: Adaptive Computation Time for Simultaneous Machine Translation—0
Towards Structured Deep Neural Network for Automatic Speech Recognition—0
Towards Structured Deep Neural Network for Automatic Speech Recognition—0
Towards the Universal Defense for Query-Based Audio Adversarial Attacks—0
Unified model for code-switching speech recognition and language identification based on a concatenated tokenizer—0
Toward Streaming ASR with Non-Autoregressive Insertion-based Model—0
Towards Understanding ASR Error Correction for Medical Conversations—0
Towards Unsupervised Automatic Speech Recognition Trained by Unaligned Speech and Text only—0
Towards Word-Level End-to-End Neural Speaker Diarization with Auxiliary Network—0
Towards Zero-Shot Code-Switched Speech Recognition—0
Toward Zero Oracle Word Error Rate on the Switchboard Benchmark—0
Tradition or Innovation: A Comparison of Modern ASR Methods for Forced Alignment—0
Training for Speech Recognition on Coprocessors—0
Training Neural Speech Recognition Systems with Synthetic Speech Augmentation—0
Training Neural Speech Recognition Systems with Synthetic Speech Augmentation—0
Training Speech Enhancement Systems with Noisy Speech Datasets—0
Training variance and performance evaluation of neural networks in speech—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