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 3951–4000 of 6433 papers

TitleStatusHype
Parallel Rescoring with Transformer for Streaming On-Device Speech Recognition—0
A Survey of Deep Active LearningCode0
Data augmentation using prosody and false starts to recognize non-native children's speechCode0
Optimising AI Training Deployments using Graph Compilers and Containers—0
Aphasic Speech Recognition using a Mixture of Speech Intelligibility Experts—0
A Survey on Evolutionary Neural Architecture Search—0
Learned Transferable Architectures Can Surpass Hand-Designed Architectures for Large Scale Speech Recognition—0
Improving Tail Performance of a Deliberation E2E ASR Model Using a Large Text Corpus—0
Machine Semiotics—0
Cross-Utterance Language Models with Acoustic Error Sampling—0
Are Neural Open-Domain Dialog Systems Robust to Speech Recognition Errors in the Dialog History? An Empirical StudyCode0
A Real-time Robot-based Auxiliary System for Risk Evaluation of COVID-19 Infection—0
Adaptation Algorithms for Neural Network-Based Speech Recognition: An OverviewCode0
Speech To Semantics: Improve ASR and NLU Jointly via All-Neural Interfaces—0
Speech Recognition using EEG signals recorded using dry electrodes—0
Large-scale Transfer Learning for Low-resource Spoken Language Understanding—0
Conv-Transformer Transducer: Low Latency, Low Frame Rate, Streamable End-to-End Speech Recognition—0
LSTM Acoustic Models Learn to Align and Pronounce with Graphemes—0
MASRI-HEADSET: A Maltese Corpus for Speech Recognition—0
Textual Echo Cancellation—0
Transfer Learning Approaches for Streaming End-to-End Speech Recognition System—0
Online Automatic Speech Recognition with Listen, Attend and Spell Model—0
Transformer with Bidirectional Decoder for Speech Recognition—0
Subword Regularization: An Analysis of Scalability and Generalization for End-to-End Automatic Speech Recognition—0
TinySpeech: Attention Condensers for Deep Speech Recognition Neural Networks on Edge Devices—0
Knowledge Distillation and Data Selection for Semi-Supervised Learning in CTC Acoustic Models—0
LRSpeech: Extremely Low-Resource Speech Synthesis and Recognition—0
Investigation of Speaker-adaptation methods in Transformer based ASR—0
Deep Learning Based Dereverberation of Temporal Envelopesfor Robust Speech Recognition—0
Federated Transfer Learning with Dynamic Gradient Aggregation—0
Iterative Compression of End-to-End ASR Model using AutoML—0
Attentive Fusion Enhanced Audio-Visual Encoding for Transformer Based Robust Speech Recognition—0
Shouted Speech Compensation for Speaker Verification Robust to Vocal Effort Conditions—0
Unsupervised Cross-Domain Singing Voice Conversion—0
A Transfer Learning Method for Speech Emotion Recognition from Automatic Speech Recognition—0
Improving End-to-End Speech-to-Intent Classification with Reptile—0
"This is Houston. Say again, please". The Behavox system for the Apollo-11 Fearless Steps Challenge (phase II)—0
Weakly Supervised Construction of ASR Systems with Massive Video Data—0
TutorNet: Towards Flexible Knowledge Distillation for End-to-End Speech Recognition—0
DataMix: Efficient Privacy-Preserving Edge-Cloud Inference—0
Future Vector Enhanced LSTM Language Model for LVCSR—0
Modular End-to-end Automatic Speech Recognition Framework for Acoustic-to-word Model—0
Utterance-Wise Meeting Transcription System Using Asynchronous Distributed Microphones—0
An Investigation on Deep Learning with Beta Stabilizer—0
Developing RNN-T Models Surpassing High-Performance Hybrid Models with Customization Capability—0
Exploiting Cross-Lingual Knowledge in Unsupervised Acoustic Modeling for Low-Resource Languages—0
Privacy-preserving Voice Analysis via Disentangled Representations—0
Neural Kalman Filtering for Speech Enhancement—0
Team Deep Mixture of Experts for Distributed Power Control—0
Efficient minimum word error rate training of RNN-Transducer for end-to-end 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