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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 2301–2350 of 3012 papers

TitleStatusHype
Deep Neural Networks for Automatic Speech Processing: A Survey from Large Corpora to Limited Data—0
Improving Uyghur ASR systems with decoders using morpheme-based language models—0
Controllable Time-Delay Transformer for Real-Time Punctuation Prediction and Disfluency Detection—0
SkinAugment: Auto-Encoding Speaker Conversions for Automatic Speech TranslationCode0
A Density Ratio Approach to Language Model Fusion in End-To-End Automatic Speech Recognition—0
Distributed Training of Deep Neural Network Acoustic Models for Automatic Speech Recognition—0
Attention-based ASR with Lightweight and Dynamic Convolutions—0
RTMobile: Beyond Real-Time Mobile Acceleration of RNNs for Speech Recognition—0
Gradient-Adjusted Neuron Activation Profiles for Comprehensive Introspection of Convolutional Speech Recognition Models—0
Speech Corpus of Ainu Folklore and End-to-end Speech Recognition for Ainu Language—0
Unsupervised Speaker Adaptation using Attention-based Speaker Memory for End-to-End ASR—0
Looking Enhances Listening: Recovering Missing Speech Using Images—0
Attentional Speech Recognition Models Misbehave on Out-of-domain UtterancesCode0
Robust Multi-channel Speech Recognition using Frequency Aligned Network—0
Continuous Silent Speech Recognition using EEG—0
Generating diverse and natural text-to-speech samples using a quantized fine-grained VAE and auto-regressive prosody prior—0
End-to-End Automatic Speech Recognition Integrated With CTC-Based Voice Activity Detection—0
Dialogue-Based Simulation For Cultural Awareness Training—0
Fully Learnable Front-End for Multi-Channel Acoustic Modeling using Semi-Supervised Learning—0
BUT Opensat 2019 Speech Recognition System—0
Environment-aware Reconfigurable Noise Suppression—0
Deep Xi as a Front-End for Robust Automatic Speech Recognition—0
Joint Contextual Modeling for ASR Correction and Language Understanding—0
Submodular Rank Aggregation on Score-based Permutations for Distributed Automatic Speech RecognitionCode0
Lattice-based Improvements for Voice Triggering Using Graph Neural Networks—0
Low-rank Gradient Approximation For Memory-Efficient On-device Training of Deep Neural Network—0
Semi-supervised ASR by End-to-end Self-training—0
TLT-school: a Corpus of Non Native Children Speech—0
Sequence Labeling Approach to the Task of Sentence Boundary DetectionCode0
Transformer-based Online CTC/attention End-to-End Speech Recognition Architecture—0
Improving Spoken Language Understanding By Exploiting ASR N-best Hypotheses—0
Improving Dysarthric Speech Intelligibility Using Cycle-consistent Adversarial Training—0
Open Challenge for Correcting Errors of Speech Recognition Systems—0
Streaming automatic speech recognition with the transformer model—0
Audio-visual Recognition of Overlapped speech for the LRS2 dataset—0
Investigation and Analysis of Hyper and Hypo neuron pruning to selectively update neurons during Unsupervised Adaptation—0
Speaker-aware speech-transformer—0
Deep Representation Learning in Speech Processing: Challenges, Recent Advances, and Future Trends—0
Attention based on-device streaming speech recognition with large speech corpus—0
EEG based Continuous Speech Recognition using Transformers—0
power-law nonlinearity with maximally uniform distribution criterion for improved neural network training in automatic speech recognition—0
Statistical Testing on ASR Performance via Blockwise Bootstrap—0
Generating Synthetic Audio Data for Attention-Based Speech Recognition Systems—0
Role of non-linear data processing on speech recognition task in the framework of reservoir computing—0
A Cycle-GAN Approach to Model Natural Perturbations in Speech for ASR Applications—0
Continuous Speech Recognition using EEG and Video—0
Synchronous Speech Recognition and Speech-to-Text Translation with Interactive DecodingCode0
On Neural Phone Recognition of Mixed-Source ECoG Signals—0
Leveraging End-to-End Speech Recognition with Neural Architecture Search—0
SpecAugment on Large Scale Datasets—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