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 42014250 of 6433 papers

TitleStatusHype
Attentional Speech Recognition Models Misbehave on Out-of-domain UtterancesCode0
End-to-End Multi-speaker Speech Recognition with Transformer0
Unsupervised pretraining transfers well across languagesCode1
Transformer Transducer: A Streamable Speech Recognition Model with Transformer Encoders and RNN-T LossCode1
Generating diverse and natural text-to-speech samples using a quantized fine-grained VAE and auto-regressive prosody prior0
Continuous Silent Speech Recognition using EEG0
Robust Multi-channel Speech Recognition using Frequency Aligned Network0
Vocoder-free End-to-End Voice Conversion with Transformer NetworkCode0
Improving Efficiency in Large-Scale Decentralized Distributed Training0
End-to-End Automatic Speech Recognition Integrated With CTC-Based Voice Activity Detection0
Dialogue-Based Simulation For Cultural Awareness Training0
Fully Learnable Front-End for Multi-Channel Acoustic Modeling using Semi-Supervised Learning0
Detecting Emotion Primitives from Speech and their use in discerning Categorical Emotions0
BUT Opensat 2019 Speech Recognition System0
Continuous speech separation: dataset and analysisCode1
Environment-aware Reconfigurable Noise Suppression0
Audio-Visual Decision Fusion for WFST-based and seq2seq Models0
Learning Robust and Multilingual Speech Representations0
Deep Xi as a Front-End for Robust Automatic Speech Recognition0
Joint Contextual Modeling for ASR Correction and Language Understanding0
Scaling Up Online Speech Recognition Using ConvNets0
Submodular Rank Aggregation on Score-based Permutations for Distributed Automatic Speech RecognitionCode0
Temporal Information Processing on Noisy Quantum Computers0
Multi-task self-supervised learning for Robust Speech RecognitionCode1
Lattice-based Improvements for Voice Triggering Using Graph Neural Networks0
Learning To Detect Keyword Parts And Whole By Smoothed Max Pooling0
Data Techniques For Online End-to-end Speech Recognition0
Semi-supervised ASR by End-to-end Self-training0
Low-rank Gradient Approximation For Memory-Efficient On-device Training of Deep Neural Network0
TLT-school: a Corpus of Non Native Children Speech0
Sequence Labeling Approach to the Task of Sentence Boundary DetectionCode0
Single headed attention based sequence-to-sequence model for state-of-the-art results on Switchboard0
FlexiBO: A Decoupled Cost-Aware Multi-Objective Optimization Approach for Deep Neural NetworksCode1
Transformer-based Online CTC/attention End-to-End Speech Recognition Architecture0
Visually Guided Self Supervised Learning of Speech Representations0
Improving Spoken Language Understanding By Exploiting ASR N-best Hypotheses0
Improving Dysarthric Speech Intelligibility Using Cycle-consistent Adversarial Training0
Open Challenge for Correcting Errors of Speech Recognition Systems0
Streaming automatic speech recognition with the transformer model0
Investigation and Analysis of Hyper and Hypo neuron pruning to selectively update neurons during Unsupervised Adaptation0
Audio-visual Recognition of Overlapped speech for the LRS2 dataset0
Character-Aware Attention-Based End-to-End Speech Recognition0
Domain Adaptation via Teacher-Student Learning for End-to-End Speech Recognition0
Transformer-based language modeling and decoding for conversational speech recognition0
Speaker-aware speech-transformer0
Deep Representation Learning in Speech Processing: Challenges, Recent Advances, and Future Trends0
Attention based on-device streaming speech recognition with large speech corpus0
Training Deep Networks with Stochastic Gradient Normalized by Layerwise Adaptive Second Moments0
EEG based Continuous Speech Recognition using Transformers0
end-to-end training of a large vocabulary end-to-end speech recognition system0
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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