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 53515400 of 6433 papers

TitleStatusHype
Character and Subword-Based Word Representation for Neural Language Modeling Prediction0
Agent-Aware Dropout DQN for Safe and Efficient On-line Dialogue Policy Learning0
Leveraging Deep Neural Network Activation Entropy to cope with Unseen Data in Speech Recognition0
Information Theoretic Analysis of DNN-HMM Acoustic Modeling0
MIT-QCRI Arabic Dialect Identification System for the 2017 Multi-Genre Broadcast Challenge0
Twin Networks: Matching the Future for Sequence GenerationCode0
Cold Fusion: Training Seq2Seq Models Together with Language Models0
The Microsoft 2017 Conversational Speech Recognition System0
Future Word Contexts in Neural Network Language Models0
An Improved Residual LSTM Architecture for Acoustic Modeling0
Large-Scale Domain Adaptation via Teacher-Student Learning0
Language Identification Using Deep Convolutional Recurrent Neural NetworksCode0
Dialogue Act Segmentation for Vietnamese Human-Human Conversational Texts0
Comparison of Decoding Strategies for CTC Acoustic Models0
Improving Speaker-Independent Lipreading with Domain-Adversarial Training0
Massively Multilingual Neural Grapheme-to-Phoneme ConversionCode0
Deep Learning for Punctuation Restoration in Medical Reports0
Enabling robust and fluid spoken dialogue with cognitively impaired users0
End-to-End Neural Segmental Models for Speech Recognition0
Utterance Intent Classification of a Spoken Dialogue System with Efficiently Untied Recursive Autoencoders0
Attentive listening system with backchanneling, response generation and flexible turn-taking0
Automated Preamble Detection in Dictated Medical Reports0
XJSA at SemEval-2017 Task 4: A Deep System for Sentiment Classification in Twitter0
Information Navigation System with Discovering User Interests0
Exploring Neural Transducers for End-to-End Speech Recognition0
Language modeling with Neural trans-dimensional random fields0
Attention-Based End-to-End Speech Recognition on Voice Search0
Progressive Joint Modeling in Unsupervised Single-channel Overlapped Speech Recognition0
Fast and Accurate OOV Decoder on High-Level Features0
Single-Channel Multi-talker Speech Recognition with Permutation Invariant Training0
Dynamic Layer Normalization for Adaptive Neural Acoustic Modeling in Speech Recognition0
Unsupervised Domain Adaptation for Robust Speech Recognition via Variational Autoencoder-Based Data Augmentation0
Encoding Word Confusion Networks with Recurrent Neural Networks for Dialog State Tracking0
Houdini: Fooling Deep Structured Prediction Models0
Listening while Speaking: Speech Chain by Deep Learning0
Automatic Speech Recognition with Very Large Conversational Finnish and Estonian Vocabularies0
Predicting Causes of Reformulation in Intelligent Assistants0
Unsupervised Submodular Rank Aggregation on Score-based PermutationsCode0
Improving LSTM-CTC based ASR performance in domains with limited training dataCode0
Dual Supervised Learning0
Accent Adaptation for the Air Traffic Control Domain0
Rank-1 Constrained Multichannel Wiener Filter for Speech Recognition in Noisy EnvironmentsCode0
A Local Detection Approach for Named Entity Recognition and Mention Detection0
Evaluating Compound Splitters Extrinsically with Textual Entailment0
PyDial: A Multi-domain Statistical Dialogue System Toolkit0
Joint CTC/attention decoding for end-to-end speech recognition0
Text-based Speaker Identification on Multiparty Dialogues Using Multi-document Convolutional Neural Networks0
Multimodal Machine Learning: Integrating Language, Vision and Speech0
SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloud0
Toward Computation and Memory Efficient Neural Network Acoustic Models with Binary Weights and Activations0
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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