SOTAVerified

Speech Recognition

Speech Recognition is the task of converting spoken language into text. It involves recognizing the words spoken in an audio recording and transcribing them into a written format. The goal is to accurately transcribe the speech in real-time or from recorded audio, taking into account factors such as accents, speaking speed, and background noise.

( Image credit: SpecAugment )

Papers

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Showing 5301–5350 of 6433 papers

TitleStatusHype
Acoustic Model Fusion for End-to-end Speech Recognition—0
Acoustic Modeling Using a Shallow CNN-HTSVM Architecture—0
Acoustic Model Optimization Based On Evolutionary Stochastic Gradient Descent with Anchors for Automatic Speech Recognition—0
Acoustic Model Optimization over Multiple Data Sources: Merging and Valuation—0
Acoustic, Phonetic and Prosodic Features of Parkinson's disease Speech—0
Acoustic-Phonetic Approach for ASR of Less Resourced Languages Using Monolingual and Cross-Lingual Information—0
Acoustics Based Intent Recognition Using Discovered Phonetic Units for Low Resource Languages—0
Acoustics-guided evaluation (AGE): a new measure for estimating performance of speech enhancement algorithms for robust ASR—0
Acoustic to Articulatory Inversion of Speech; Data Driven Approaches, Challenges, Applications, and Future Scope—0
Acoustic-to-articulatory Speech Inversion with Multi-task Learning—0
Acoustic-to-Word Models with Conversational Context Information—0
Acoustic-to-Word Recognition with Sequence-to-Sequence Models—0
Acoustic Word Disambiguation with Phonogical Features in Danish ASR—0
A CRF Sequence Labeling Approach to Chinese Punctuation Prediction—0
A Critical Review of Physics-Informed Machine Learning Applications in Subsurface Energy Systems—0
A Cross-language Study on Automatic Speech Disfluency Detection—0
A Crowdsourcing Smartphone Application for Swiss German: Putting Language Documentation in the Hands of the Users—0
A CTC Alignment-based Non-autoregressive Transformer for End-to-end Automatic Speech Recognition—0
A CTC Triggered Siamese Network with Spatial-Temporal Dropout for Speech Recognition—0
Active and Semi-Supervised Learning in ASR: Benefits on the Acoustic and Language Models—0
Active Learning Based Fine-Tuning Framework for Speech Emotion Recognition—0
Active Learning for Speech Recognition: the Power of Gradients—0
Activity focused Speech Recognition of Preschool Children in Early Childhood Classrooms—0
A Curriculum Learning Method for Improved Noise Robustness in Automatic Speech Recognition—0
A Cycle-GAN Approach to Model Natural Perturbations in Speech for ASR Applications—0
ADAGIO: Interactive Experimentation with Adversarial Attack and Defense for Audio—0
Adam^+: A Stochastic Method with Adaptive Variance Reduction—0
AdaMER-CTC: Connectionist Temporal Classification with Adaptive Maximum Entropy Regularization for Automatic Speech Recognition—0
Adam Induces Implicit Weight Sparsity in Rectifier Neural Networks—0
Adaptable End-to-End ASR Models using Replaceable Internal LMs and Residual Softmax—0
Adapt-and-Adjust: Overcoming the Long-Tail Problem of Multilingual Speech Recognition—0
Adaptation and Optimization of Automatic Speech Recognition (ASR) for the Maritime Domain in the Field of VHF Communication—0
Adaptation Data Selection using Neural Language Models: Experiments in Machine Translation—0
Adapter-Based Multi-Agent AVSR Extension for Pre-Trained ASR Models—0
Adapting a FrameNet Semantic Parser for Spoken Language Understanding Using Adversarial Learning—0
Adapting an Unadaptable ASR System—0
Adapting Automatic Speech Recognition for Accented Air Traffic Control Communications—0
Adapting general-purpose speech recognition engine output for domain-specific natural language question answering—0
Adapting GPT, GPT-2 and BERT Language Models for Speech Recognition—0
Adapting Multi-Lingual ASR Models for Handling Multiple Talkers—0
Adapting Multilingual Speech Representation Model for a New, Underresourced Language through Multilingual Fine-tuning and Continued Pretraining—0
Adapting OpenAI's Whisper for Speech Recognition on Code-Switch Mandarin-English SEAME and ASRU2019 Datasets—0
Adapting self-supervised models to multi-talker speech recognition using speaker embeddings—0
Adapting Text-based Dialogue State Tracker for Spoken Dialogues—0
Adapting Whisper for Code-Switching through Encoding Refining and Language-Aware Decoding—0
Adapting Whisper for Regional Dialects: Enhancing Public Services for Vulnerable Populations in the United Kingdom—0
Adaptive Activation Network For Low Resource Multilingual Speech Recognition—0
Adaptive Audio-Visual Speech Recognition via Matryoshka-Based Multimodal LLMs—0
Adaptive Axonal Delays in feedforward spiking neural networks for accurate spoken word recognition—0
Adaptive Contextual Biasing for Transducer Based Streaming Speech Recognition—0
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All datasetsLibriSpeech test-cleanLibriSpeech test-otherSwitchboard + Hub500TIMITAISHELL-1WSJ eval92Common Voice Germanswb_hub_500 WER fullSWBCHTUDACommon Voice FrenchCommon Voice SpanishMediaSpeech

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AmNetWord Error Rate (WER)8.6—Unverified
2HMM-(SAT)GMMWord Error Rate (WER)8—Unverified
3Local Prior Matching (Large Model)Word Error Rate (WER)7.19—Unverified
4SnipsWord Error Rate (WER)6.4—Unverified
5Li-GRUWord Error Rate (WER)6.2—Unverified
6HMM-DNN + pNorm*Word Error Rate (WER)5.5—Unverified
7CTC + policy learningWord Error Rate (WER)5.42—Unverified
8Deep Speech 2Word Error Rate (WER)5.33—Unverified
9HMM-TDNN + iVectorsWord Error Rate (WER)4.8—Unverified
10Gated ConvNetsWord Error Rate (WER)4.8—Unverified
#ModelMetricClaimedVerifiedStatus
1Local Prior Matching (Large Model)Word Error Rate (WER)20.84—Unverified
2SnipsWord Error Rate (WER)16.5—Unverified
3Local Prior Matching (Large Model, ConvLM LM)Word Error Rate (WER)15.28—Unverified
4Deep Speech 2Word Error Rate (WER)13.25—Unverified
5TDNN + pNorm + speed up/down speechWord Error Rate (WER)12.5—Unverified
6CTC-CRF 4gram-LMWord Error Rate (WER)10.65—Unverified
7Convolutional Speech RecognitionWord Error Rate (WER)10.47—Unverified
8MT4SSLWord Error Rate (WER)9.6—Unverified
9Jasper DR 10x5Word Error Rate (WER)8.79—Unverified
10EspressoWord Error Rate (WER)8.7—Unverified
#ModelMetricClaimedVerifiedStatus
1Deep SpeechPercentage error20—Unverified
2DNN-HMMPercentage error18.5—Unverified
3CD-DNNPercentage error16.1—Unverified
4DNNPercentage error16—Unverified
5DNN + DropoutPercentage error15—Unverified
6DNN BMMIPercentage error12.9—Unverified
7DNN MPEPercentage error12.9—Unverified
8DNN MMIPercentage error12.9—Unverified
9HMM-TDNN + pNorm + speed up/down speechPercentage error12.9—Unverified
10HMM-DNN +sMBRPercentage error12.6—Unverified
#ModelMetricClaimedVerifiedStatus
1LSNNPercentage error33.2—Unverified
2LAS multitask with indicators samplingPercentage error20.4—Unverified
3Soft Monotonic Attention (ours, offline)Percentage error20.1—Unverified
4QCNN-10L-256FMPercentage error19.64—Unverified
5Bi-LSTM + skip connections w/ CTCPercentage error17.7—Unverified
6Bi-RNN + AttentionPercentage error17.6—Unverified
7RNN-CRF on 24(x3) MFSCPercentage error17.3—Unverified
8CNN in time and frequency + dropout, 17.6% w/o dropoutPercentage error16.7—Unverified
9Light Gated Recurrent UnitsPercentage error16.7—Unverified
10GRUPercentage error16.6—Unverified
#ModelMetricClaimedVerifiedStatus
1AttWord Error Rate (WER)18.7—Unverified
2CTC/AttWord Error Rate (WER)6.7—Unverified
3BRA-EWord Error Rate (WER)6.63—Unverified
4CTC-CRF 4gram-LMWord Error Rate (WER)6.34—Unverified
5BATWord Error Rate (WER)4.97—Unverified
6ParaformerWord Error Rate (WER)4.95—Unverified
7U2Word Error Rate (WER)4.72—Unverified
8UMAWord Error Rate (WER)4.7—Unverified
9Lightweight TransducerWord Error Rate (WER)4.31—Unverified
10CIF-HKD With LMWord Error Rate (WER)4.1—Unverified
#ModelMetricClaimedVerifiedStatus
1Jasper 10x3Word Error Rate (WER)6.9—Unverified
2CNN over RAW speech (wav)Word Error Rate (WER)5.6—Unverified
3CTC-CRF 4gram-LMWord Error Rate (WER)3.79—Unverified
4Deep Speech 2Word Error Rate (WER)3.6—Unverified
5test-set on open vocabulary (i.e. harder), model = HMM-DNN + pNorm*Word Error Rate (WER)3.6—Unverified
6Convolutional Speech RecognitionWord Error Rate (WER)3.5—Unverified
7TC-DNN-BLSTM-DNNWord Error Rate (WER)3.5—Unverified
8EspressoWord Error Rate (WER)3.4—Unverified
9CTC-CRF VGG-BLSTMWord Error Rate (WER)3.2—Unverified
10Transformer with Relaxed AttentionWord Error Rate (WER)3.19—Unverified