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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 1751–1775 of 3012 papers

TitleStatusHype
Phonological Pun-derstanding—0
Pitch-Aware RNN-T for Mandarin Chinese Mispronunciation Detection and Diagnosis—0
PI-Whisper: Designing an Adaptive and Incremental Automatic Speech Recognition System for Edge Devices—0
pMCT: Patched Multi-Condition Training for Robust Speech Recognition—0
PM-MMUT: Boosted Phone-Mask Data Augmentation using Multi-Modeling Unit Training for Phonetic-Reduction-Robust E2E Speech Recognition—0
Polyphonic pitch detection with convolutional recurrent neural networks—0
Post-decoder Biasing for End-to-End Speech Recognition of Multi-turn Medical Interview—0
Post-Stroke Speech Transcription Challenge (Task B): Correctness Detection in Anomia Diagnosis with Imperfect Transcripts—0
power-law nonlinearity with maximally uniform distribution criterion for improved neural network training in automatic speech recognition—0
Practical Application of Domain Dependent Confidence Measurement for Spoken Language Understanding Systems—0
Practical Speech Recognition with HTK—0
Precise Detection of Speech Endpoints Dynamically: A Wavelet Convolution based approach—0
Predicting Causes of Reformulation in Intelligent Assistants—0
Predicting Compact Phrasal Rewrites with Large Language Models for ASR Post Editing—0
Predicting lexical skills from oral reading with acoustic measures—0
Predicting Multi-Codebook Vector Quantization Indexes for Knowledge Distillation—0
Predicting positive transfer for improved low-resource speech recognition using acoustic pseudo-tokens—0
Prediction of Listener Perception of Argumentative Speech in a Crowdsourced Dataset Using (Psycho-)Linguistic and Fluency Features—0
Prediction of speech intelligibility with DNN-based performance measures—0
Predictive Speech Recognition and End-of-Utterance Detection Towards Spoken Dialog Systems—0
Preliminary Study on SSCF-derived Polar Coordinate for ASR—0
Preparation of Bangla Speech Corpus from Publicly Available Audio \& Text—0
Pretraining Approaches for Spoken Language Recognition: TalTech Submission to the OLR 2021 Challenge—0
Pre-training for Spoken Language Understanding with Joint Textual and Phonetic Representation Learning—0
Pre-training in Deep Reinforcement Learning for Automatic 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