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

Showing 60016050 of 6433 papers

TitleStatusHype
Best of Both Worlds: Robust Accented Speech Recognition with Adversarial Transfer Learning0
Best Practices for Crowdsourcing Dialectal Arabic Speech Transcription0
Best Practices for Noise-Based Augmentation to Improve the Performance of Deployable Speech-Based Emotion Recognition Systems0
Best Practices for Noise-Based Augmentation to Improve the Performance of Deployable Speech-Based Emotion Recognition Systems0
Better Pseudo-labeling with Multi-ASR Fusion and Error Correction by SpeechLLM0
Better Transcription of UK Supreme Court Hearings0
Beyond Binary: Multiclass Paraphasia Detection with Generative Pretrained Transformers and End-to-End Models0
Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systems0
Beyond Classification: Towards Speech Emotion Reasoning with Multitask AudioLLMs0
Beyond Manual Transcripts: The Potential of Automated Speech Recognition Errors in Improving Alzheimer's Disease Detection0
Beyond Novelty Detection: Incongruent Events, when General and Specific Classifiers Disagree0
Beyond the Labels: Unveiling Text-Dependency in Paralinguistic Speech Recognition Datasets0
Beyond Universal Transformer: block reusing with adaptor in Transformer for automatic speech recognition0
Bi-APC: Bidirectional Autoregressive Predictive Coding for Unsupervised Pre-training and Its Application to Children's ASR0
Biased Self-supervised learning for ASR0
Biber Redux: Reconsidering Dimensions of Variation in American English0
Bi-directional Context-Enhanced Speech Large Language Models for Multilingual Conversational ASR0
Bidirectional Representations for Low Resource Spoken Language Understanding0
Bidirectional RNN for Medical Event Detection in Electronic Health Records0
Bifocal Neural ASR: Exploiting Keyword Spotting for Inference Optimization0
BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition0
Bilevel Joint Unsupervised and Supervised Training for Automatic Speech Recognition0
Bilingual End-to-End ASR with Byte-Level Subwords0
Bilingual Streaming ASR with Grapheme units and Auxiliary Monolingual Loss0
Binarized LSTM Language Model0
Binary classification of spoken words with passive phononic metamaterials0
BioDCA Identifier: A System for Automatic Identification of Discourse Connective and Arguments from Biomedical Text0
Bio-Inspired Mamba: Temporal Locality and Bioplausible Learning in Selective State Space Models0
Birzeit Arabic Dialect Identification System for the 2018 VarDial Challenge0
Catch Me If You Can: Blackbox Adversarial Attacks on Automatic Speech Recognition using Frequency Masking0
Blank-regularized CTC for Frame Skipping in Neural Transducer0
Blending LLMs into Cascaded Speech Translation: KIT's Offline Speech Translation System for IWSLT 20240
Blending LSTMs into CNNs0
Blind and neural network-guided convolutional beamformer for joint denoising, dereverberation, and source separation0
Blind dereverberation of single channel speech signal based on harmonic structure0
Blind phoneme segmentation with temporal prediction errors0
Blind Signal Dereverberation for Machine Speech Recognition0
Block-Sparse Recurrent Neural Networks0
Blockwise Streaming Transformer for Spoken Language Understanding and Simultaneous Speech Translation0
BLSTM-Based Confidence Estimation for End-to-End Speech Recognition0
Book Reviews: Robots that Talk and Listen edited by Judith A. Markowitz0
Boosting Chinese ASR Error Correction with Dynamic Error Scaling Mechanism0
Boosting Code-Switching ASR with Mixture of Experts Enhanced Speech-Conditioned LLM0
Boosting End-to-End Multilingual Phoneme Recognition through Exploiting Universal Speech Attributes Constraints0
Boosting Hybrid Autoregressive Transducer-based ASR with Internal Acoustic Model Training and Dual Blank Thresholding0
Boosting Local Spectro-Temporal Features for Speech Analysis0
Boosting LSTM Performance Through Dynamic Precision Selection0
Boosting Noise Robustness of Acoustic Model via Deep Adversarial Training0
Boosting Norwegian Automatic Speech Recognition0
Boosting Punctuation Restoration with Data Generation and Reinforcement Learning0
Show:102550
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AmNetWord Error Rate (WER)8.6Unverified
2HMM-(SAT)GMMWord Error Rate (WER)8Unverified
3Local Prior Matching (Large Model)Word Error Rate (WER)7.19Unverified
4SnipsWord Error Rate (WER)6.4Unverified
5Li-GRUWord Error Rate (WER)6.2Unverified
6HMM-DNN + pNorm*Word Error Rate (WER)5.5Unverified
7CTC + policy learningWord Error Rate (WER)5.42Unverified
8Deep Speech 2Word Error Rate (WER)5.33Unverified
9HMM-TDNN + iVectorsWord Error Rate (WER)4.8Unverified
10Gated ConvNetsWord Error Rate (WER)4.8Unverified
#ModelMetricClaimedVerifiedStatus
1Local Prior Matching (Large Model)Word Error Rate (WER)20.84Unverified
2SnipsWord Error Rate (WER)16.5Unverified
3Local Prior Matching (Large Model, ConvLM LM)Word Error Rate (WER)15.28Unverified
4Deep Speech 2Word Error Rate (WER)13.25Unverified
5TDNN + pNorm + speed up/down speechWord Error Rate (WER)12.5Unverified
6CTC-CRF 4gram-LMWord Error Rate (WER)10.65Unverified
7Convolutional Speech RecognitionWord Error Rate (WER)10.47Unverified
8MT4SSLWord Error Rate (WER)9.6Unverified
9Jasper DR 10x5Word Error Rate (WER)8.79Unverified
10EspressoWord Error Rate (WER)8.7Unverified
#ModelMetricClaimedVerifiedStatus
1Deep SpeechPercentage error20Unverified
2DNN-HMMPercentage error18.5Unverified
3CD-DNNPercentage error16.1Unverified
4DNNPercentage error16Unverified
5DNN + DropoutPercentage error15Unverified
6DNN BMMIPercentage error12.9Unverified
7DNN MPEPercentage error12.9Unverified
8DNN MMIPercentage error12.9Unverified
9HMM-TDNN + pNorm + speed up/down speechPercentage error12.9Unverified
10HMM-DNN +sMBRPercentage error12.6Unverified
#ModelMetricClaimedVerifiedStatus
1LSNNPercentage error33.2Unverified
2LAS multitask with indicators samplingPercentage error20.4Unverified
3Soft Monotonic Attention (ours, offline)Percentage error20.1Unverified
4QCNN-10L-256FMPercentage error19.64Unverified
5Bi-LSTM + skip connections w/ CTCPercentage error17.7Unverified
6Bi-RNN + AttentionPercentage error17.6Unverified
7RNN-CRF on 24(x3) MFSCPercentage error17.3Unverified
8CNN in time and frequency + dropout, 17.6% w/o dropoutPercentage error16.7Unverified
9Light Gated Recurrent UnitsPercentage error16.7Unverified
10GRUPercentage error16.6Unverified
#ModelMetricClaimedVerifiedStatus
1AttWord Error Rate (WER)18.7Unverified
2CTC/AttWord Error Rate (WER)6.7Unverified
3BRA-EWord Error Rate (WER)6.63Unverified
4CTC-CRF 4gram-LMWord Error Rate (WER)6.34Unverified
5BATWord Error Rate (WER)4.97Unverified
6ParaformerWord Error Rate (WER)4.95Unverified
7U2Word Error Rate (WER)4.72Unverified
8UMAWord Error Rate (WER)4.7Unverified
9Lightweight TransducerWord Error Rate (WER)4.31Unverified
10CIF-HKD With LMWord Error Rate (WER)4.1Unverified
#ModelMetricClaimedVerifiedStatus
1Jasper 10x3Word Error Rate (WER)6.9Unverified
2CNN over RAW speech (wav)Word Error Rate (WER)5.6Unverified
3CTC-CRF 4gram-LMWord Error Rate (WER)3.79Unverified
4Deep Speech 2Word Error Rate (WER)3.6Unverified
5test-set on open vocabulary (i.e. harder), model = HMM-DNN + pNorm*Word Error Rate (WER)3.6Unverified
6Convolutional Speech RecognitionWord Error Rate (WER)3.5Unverified
7TC-DNN-BLSTM-DNNWord Error Rate (WER)3.5Unverified
8EspressoWord Error Rate (WER)3.4Unverified
9CTC-CRF VGG-BLSTMWord Error Rate (WER)3.2Unverified
10Transformer with Relaxed AttentionWord Error Rate (WER)3.19Unverified