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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 2576–2600 of 3012 papers

TitleStatusHype
Contextual Speech Recognition with Difficult Negative Training Examples—0
Cascaded CNN-resBiLSTM-CTC: An End-to-End Acoustic Model For Speech Recognition—0
Language Modeling for Code-Switching: Evaluation, Integration of Monolingual Data, and Discriminative TrainingCode0
Speaker Selective Beamformer with Keyword Mask Estimation—0
The MeMAD Submission to the IWSLT 2018 Speech Translation Task—0
A Deep Generative Acoustic Model for Compositional Automatic Speech Recognition—0
Semi-supervised acoustic model training for speech with code-switching—0
Transferable and Configurable Audio Adversarial Attack from Low-Level Features—0
On the Inductive Bias of Word-Character-Level Multi-Task Learning for Speech Recognition—0
ROBUST SPEECH COMMAND RECOGNITION USING LABEL-DRIVEN TIME-FREQUENCY MASKING—0
Cycle-Consistent GAN Front-End to Improve ASR Robustness to Perturbed Speech—0
How transferable are features in convolutional neural network acoustic models across languages?—0
Targeted Adversarial Examples for Black Box Audio Systems—0
Training Neural Speech Recognition Systems with Synthetic Speech Augmentation—0
Exploring Textual and Speech information in Dialogue Act Classification with Speaker Domain Adaptation—0
Speech Recognition with Quaternion Neural Networks—0
Robust Neural Machine Translation with Joint Textual and Phonetic Embedding—0
The Sogou-TIIC Speech Translation System for IWSLT 2018—0
The AFRL IWSLT 2018 Systems: What Worked, What Didn’t—0
Using Spoken Word Posterior Features in Neural Machine Translation—0
Neural Speech Translation at AppTek—0
會議語音辨識使用語者資訊之語言模型調適技術 (On the Use of Speaker-Aware Language Model Adaptation Techniques for Meeting Speech Recognition ) [In Chinese]—0
探討聲學模型的合併技術與半監督鑑別式訓練於會議語音辨識之研究 (Investigating acoustic model combination and semi-supervised discriminative training for meeting speech recognition) [In Chinese]—0
Research Challenges in Building a Voice-based Artificial Personal Shopper - Position Paper—0
Improving Neural Language Models with Weight Norm Initialization and Regularization—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