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 50015050 of 6433 papers

TitleStatusHype
Unsupervised and Efficient Vocabulary Expansion for Recurrent Neural Network Language Models in ASR0
Contextual Language Model Adaptation for Conversational Agents0
Deep Reinforcement Learning: An Overview0
Evaluating Gammatone Frequency Cepstral Coefficients with Neural Networks for Emotion Recognition from SpeechCode0
Persistent Hidden States and Nonlinear Transformation for Long Short-Term Memory0
Towards Automated Single Channel Source Separation using Neural Networks0
Quaternion Convolutional Neural Networks for End-to-End Automatic Speech RecognitionCode0
Recommending Scientific Videos based on Metadata Enrichment using Linked Open Data0
Recurrent DNNs and its Ensembles on the TIMIT Phone Recognition TaskCode0
End-to-End Speech Recognition From the Raw WaveformCode0
A Survey of Recent DNN Architectures on the TIMIT Phone Recognition TaskCode0
Speaker Adapted Beamforming for Multi-Channel Automatic Speech Recognition0
Semi-tied Units for Efficient Gating in LSTM and Highway Networks0
Extending Recurrent Neural Aligner for Streaming End-to-End Speech Recognition in Mandarin0
Study of Semi-supervised Approaches to Improving English-Mandarin Code-Switching Speech Recognition0
RAPIDNN: In-Memory Deep Neural Network Acceleration Framework0
Deep Lip Reading: a comparison of models and an online application0
Nearly Zero-Shot Learning for Semantic Decoding in Spoken Dialogue Systems0
Unsupervised Adaptation with Interpretable Disentangled Representations for Distant Conversational Speech Recognition0
A Study of Enhancement, Augmentation, and Autoencoder Methods for Domain Adaptation in Distant Speech Recognition0
Quaternion Recurrent Neural NetworksCode0
Multilingual End-to-End Speech Recognition with A Single Transformer on Low-Resource Languages0
Domain Adversarial Training for Accented Speech Recognition0
Training Augmentation with Adversarial Examples for Robust Speech Recognition0
LSTM Benchmarks for Deep Learning FrameworksCode0
An Explainable Adversarial Robustness Metric for Deep Learning Neural Networks0
Making Convolutional Networks Recurrent for Visual Sequence Learning0
Dialog Generation Using Multi-Turn Reasoning Neural Networks0
Binarized LSTM Language Model0
From dictations to clinical reports using machine translation0
SMILEE: Symmetric Multi-modal Interactions with Language-gesture Enabled (AI) Embodiment0
Practical Application of Domain Dependent Confidence Measurement for Spoken Language Understanding Systems0
Role-specific Language Models for Processing Recorded Neuropsychological Exams0
Efficient Sequence Learning with Group Recurrent Networks0
Learning Hidden Unit Contribution for Adapting Neural Machine Translation Models0
An automated medical scribe for documenting clinical encounters0
Generative Bridging Network for Neural Sequence Prediction0
Atypical Inputs in Educational Applications0
End-to-end named entity extraction from speech0
ADAGIO: Interactive Experimentation with Adversarial Attack and Defense for Audio0
Grow and Prune Compact, Fast, and Accurate LSTMs0
Towards Lipreading Sentences with Active Appearance Models0
Multimodal Speaker Segmentation and Diarization using Lexical and Acoustic Cues via Sequence to Sequence Neural Networks0
Universality of Deep Convolutional Neural Networks0
Accelerating CNN inference on FPGAs: A Survey0
Automatic context window composition for distant speech recognition0
Geometric Understanding of Deep Learning0
Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfacesCode1
Mixed-Precision Training for NLP and Speech Recognition with OpenSeq2SeqCode0
Task-dependent modulation of the visual sensory thalamus assists visual-speech recognition0
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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