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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 2101–2125 of 3012 papers

TitleStatusHype
Subword Regularization: An Analysis of Scalability and Generalization for End-to-End Automatic Speech Recognition—0
LRSpeech: Extremely Low-Resource Speech Synthesis and Recognition—0
Distilling the Knowledge of BERT for Sequence-to-Sequence ASRCode1
Word Error Rate Estimation Without ASR Output: e-WER2Code1
Deep Learning Based Dereverberation of Temporal Envelopesfor Robust Speech Recognition—0
Investigation of Speaker-adaptation methods in Transformer based ASR—0
Pretraining Techniques for Sequence-to-Sequence Voice ConversionCode1
Unsupervised Cross-Domain Singing Voice Conversion—0
A Transfer Learning Method for Speech Emotion Recognition from Automatic Speech Recognition—0
Iterative Compression of End-to-End ASR Model using AutoML—0
Shouted Speech Compensation for Speaker Verification Robust to Vocal Effort Conditions—0
"This is Houston. Say again, please". The Behavox system for the Apollo-11 Fearless Steps Challenge (phase II)—0
Weakly Supervised Construction of ASR Systems with Massive Video Data—0
Modular End-to-end Automatic Speech Recognition Framework for Acoustic-to-word Model—0
Utterance-Wise Meeting Transcription System Using Asynchronous Distributed Microphones—0
Developing RNN-T Models Surpassing High-Performance Hybrid Models with Customization Capability—0
Exploiting Cross-Lingual Knowledge in Unsupervised Acoustic Modeling for Low-Resource Languages—0
Neural Kalman Filtering for Speech Enhancement—0
Effects of Language Relatedness for Cross-lingual Transfer Learning in Character-Based Language Models—0
Audio Adversarial Examples for Robust Hybrid CTC/Attention Speech Recognition—0
Towards an Automated SOAP Note: Classifying Utterances from Medical Conversations—0
Neural Architecture Search For LF-MMI Trained Time Delay Neural Networks—0
SoK: The Faults in our ASRs: An Overview of Attacks against Automatic Speech Recognition and Speaker Identification Systems—0
Fast Transformers with Clustered AttentionCode2
Massively Multilingual ASR: 50 Languages, 1 Model, 1 Billion Parameters—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