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 43014350 of 6433 papers

TitleStatusHype
Improving Non-Intrusive Load Disaggregation through an Attention-Based Deep Neural NetworkCode0
Independent and automatic evaluation of acoustic-to-articulatory inversion modelsCode0
3-D Feature and Acoustic Modeling for Far-Field Speech Recognition0
The Deep Learning Revolution and Its Implications for Computer Architecture and Chip Design0
Privacy-Preserving Adversarial Representation Learning in ASR: Reality or Illusion?0
Long-span language modeling for speech recognition0
Data Efficient Direct Speech-to-Text Translation with Modality Agnostic Meta-Learning0
Listen and Fill in the Missing Letters: Non-Autoregressive Transformer for Speech Recognition0
Multimodal Intelligence: Representation Learning, Information Fusion, and Applications0
Effectiveness of self-supervised pre-training for speech recognitionCode1
Evaluating Voice Conversion-based Privacy Protection against Informed Attackers0
Speaker Adaptation for Attention-Based End-to-End Speech Recognition0
A Simplified Fully Quantized Transformer for End-to-end Speech RecognitionCode0
Recurrent Neural Network Transducer for Audio-Visual Speech RecognitionCode0
Europarl-ST: A Multilingual Corpus For Speech Translation Of Parliamentary Debates0
Investigation of Error Simulation Techniques for Learning Dialog Policies for Conversational Error Recovery0
Boosting LSTM Performance Through Dynamic Precision Selection0
A comparison of end-to-end models for long-form speech recognition0
RNN-T For Latency Controlled ASR With Improved Beam Search0
Supervised level-wise pretraining for recurrent neural network initialization in multi-class classification0
SHARP: An Adaptable, Energy-Efficient Accelerator for Recurrent Neural Network0
What does a network layer hear? Analyzing hidden representations of end-to-end ASR through speech synthesisCode0
Onssen: an open-source speech separation and enhancement libraryCode0
Who is Real Bob? Adversarial Attacks on Speaker Recognition SystemsCode0
The IWSLT 2019 KIT Speech Translation System0
Multi-Task Modeling of Phonographic Languages: Translating Middle Egyptian Hieroglyphs0
Data Augmentation for End-to-End Speech Translation: FBK@IWSLT ‘190
Improving Generalization of Transformer for Speech Recognition with Parallel Schedule Sampling and Relative Positional Embedding0
Chameleon: A Language Model Adaptation Toolkit for Automatic Speech Recognition of Conversational Speech0
PyOpenDial: A Python-based Domain-Independent Toolkit for Developing Spoken Dialogue Systems with Probabilistic Rules0
Entity resolution for noisy ASR transcripts0
Long-distance Detection of Bioacoustic Events with Per-channel Energy Normalization0
A neural document language modeling framework for spoken document retrieval0
Multi-scale Octave Convolutions for Robust Speech Recognition0
Lightweight and Efficient End-to-End Speech Recognition Using Low-Rank Transformer0
Does Speech enhancement of publicly available data help build robust Speech Recognition Systems?0
Improving sequence-to-sequence speech recognition training with on-the-fly data augmentation0
LeanConvNets: Low-cost Yet Effective Convolutional Neural Networks0
Transformer-based Cascaded Multimodal Speech Translation0
Unsupervised pre-training for sequence to sequence speech recognition0
DFSMN-SAN with Persistent Memory Model for Automatic Speech Recognition0
Sequence-to-sequence Automatic Speech Recognition with Word Embedding Regularization and Fused Decoding0
Towards Unsupervised Speech Recognition and Synthesis with Quantized Speech Representation Learning0
Transformer-Transducer: End-to-End Speech Recognition with Self-Attention0
Training ASR models by Generation of Contextual Information0
Meta Learning for End-to-End Low-Resource Speech Recognition0
Towards Online End-to-end Transformer Automatic Speech Recognition0
Confidence Estimation for Black Box Automatic Speech Recognition Systems Using Lattice Recurrent Neural NetworksCode0
L2RS: A Learning-to-Rescore Mechanism for Automatic Speech Recognition0
SpeechBERT: An Audio-and-text Jointly Learned Language Model for End-to-end Spoken Question Answering0
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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