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 40014050 of 6433 papers

TitleStatusHype
An End-to-End Mispronunciation Detection System for L2 English Speech Leveraging Novel Anti-Phone Modeling0
Adapting End-to-End Speech Recognition for Readable SubtitlesCode1
Detecting Adversarial Examples for Speech Recognition via Uncertainty QuantificationCode0
Low-Latency Sequence-to-Sequence Speech Recognition and Translation by Partial Hypothesis SelectionCode0
End-to-end Named Entity Recognition from English SpeechCode1
ASAPP-ASR: Multistream CNN and Self-Attentive SRU for SOTA Speech Recognition0
Simplified Self-Attention for Transformer-based End-to-End Speech Recognition0
Multistream CNN for Robust Acoustic Modeling0
Large scale evaluation of importance maps in automatic speech recognition0
Investigation of Large-Margin Softmax in Neural Language Modeling0
PyChain: A Fully Parallelized PyTorch Implementation of LF-MMI for End-to-End ASRCode1
Relative Positional Encoding for Speech Recognition and Direct Translation0
An Adversarial Approach for Explaining the Predictions of Deep Neural NetworksCode0
A Comparison of Label-Synchronous and Frame-Synchronous End-to-End Models for Speech Recognition0
Early Stage LM Integration Using Local and Global Log-Linear Combination0
A Further Study of Unsupervised Pre-training for Transformer Based Speech RecognitionCode1
GEV Beamforming Supported by DOA-based Masks Generated on Pairs of MicrophonesCode1
Improving Proper Noun Recognition in End-to-End ASR By Customization of the MWER Loss Criterion0
Exploring Transformers for Large-Scale Speech Recognition0
Deep learning approaches for neural decoding: from CNNs to LSTMs and spikes to fMRI0
Iterative Pseudo-Labeling for Speech RecognitionCode0
A systematic comparison of grapheme-based vs. phoneme-based label units for encoder-decoder-attention models0
Generative Adversarial Training Data Adaptation for Very Low-resource Automatic Speech RecognitionCode0
Enhancing Monotonic Multihead Attention for Streaming ASRCode1
Improved Noisy Student Training for Automatic Speech RecognitionCode1
Faster, Simpler and More Accurate Hybrid ASR Systems Using Wordpieces0
Should we hard-code the recurrence concept or learn it instead ? Exploring the Transformer architecture for Audio-Visual Speech RecognitionCode1
Distilling Knowledge from Ensembles of Acoustic Models for Joint CTC-Attention End-to-End Speech RecognitionCode1
Audio-visual Multi-channel Recognition of Overlapped Speech0
Attention-based Transducer for Online Speech Recognition0
Weak-Attention Suppression For Transformer Based Speech Recognition0
An Effective End-to-End Modeling Approach for Mispronunciation Detection0
Quaternion Neural Networks for Multi-channel Distant Speech Recognition0
Speech to Text Adaptation: Towards an Efficient Cross-Modal Distillation0
Dynamic Sparsity Neural Networks for Automatic Speech Recognition0
Large scale weakly and semi-supervised learning for low-resource video ASR0
Speech Recognition and Multi-Speaker Diarization of Long ConversationsCode1
That Sounds Familiar: an Analysis of Phonetic Representations Transfer Across Languages0
A Deep Learning based Wearable Healthcare IoT Device for AI-enabled Hearing Assistance Automation0
Conformer: Convolution-augmented Transformer for Speech RecognitionCode3
AccentDB: A Database of Non-Native English Accents to Assist Neural Speech Recognition0
Reducing Spelling Inconsistencies in Code-Switching ASR using Contextualized CTC Loss0
Spike-Triggered Non-Autoregressive Transformer for End-to-End Speech Recognition0
Contextualizing ASR Lattice Rescoring with Hybrid Pointer Network Language Model0
Context-Dependent Acoustic Modeling without Explicit Phone Clustering0
Coupled Training of Sequence-to-Sequence Models for Accented Speech RecognitionCode0
You Do Not Need More Data: Improving End-To-End Speech Recognition by Text-To-Speech Data Augmentation0
DARTS-ASR: Differentiable Architecture Search for Multilingual Speech Recognition and Adaptation0
Automatic Estimation of Intelligibility Measure for Consonants in Speech0
DiscreTalk: Text-to-Speech as a Machine Translation Problem0
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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