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
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
Massively Multilingual Neural Grapheme-to-Phoneme ConversionCode0
Improving Speaker-Independent Lipreading with Domain-Adversarial Training0
Information Navigation System with Discovering User Interests0
Attentive listening system with backchanneling, response generation and flexible turn-taking0
Enabling robust and fluid spoken dialogue with cognitively impaired users0
Utterance Intent Classification of a Spoken Dialogue System with Efficiently Untied Recursive Autoencoders0
Deep Learning for Punctuation Restoration in Medical Reports0
Automated Preamble Detection in Dictated Medical Reports0
XJSA at SemEval-2017 Task 4: A Deep System for Sentiment Classification in Twitter0
End-to-End Neural Segmental Models for Speech Recognition0
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
Predicting Causes of Reformulation in Intelligent Assistants0
Automatic Speech Recognition with Very Large Conversational Finnish and Estonian Vocabularies0
Unsupervised Submodular Rank Aggregation on Score-based PermutationsCode0
Dual Supervised Learning0
Improving LSTM-CTC based ASR performance in domains with limited training dataCode0
Multimodal Machine Learning: Integrating Language, Vision and Speech0
PyDial: A Multi-domain Statistical Dialogue System Toolkit0
Text-based Speaker Identification on Multiparty Dialogues Using Multi-document Convolutional Neural Networks0
Accent Adaptation for the Air Traffic Control Domain0
Evaluating Compound Splitters Extrinsically with Textual Entailment0
Joint CTC/attention decoding for end-to-end speech recognition0
A Local Detection Approach for Named Entity Recognition and Mention Detection0
Rank-1 Constrained Multichannel Wiener Filter for Speech Recognition in Noisy EnvironmentsCode0
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
Acoustic Modeling Using a Shallow CNN-HTSVM Architecture0
A Variational EM Method for Pole-Zero Modeling of Speech with Mixed Block Sparse and Gaussian Excitation0
Automatic Quality Estimation for ASR System Combination0
Comparison of Time-Frequency Representations for Environmental Sound Classification using Convolutional Neural Networks0
Analysis of dropout learning regarded as ensemble learning0
Chemception: A Deep Neural Network with Minimal Chemistry Knowledge Matches the Performance of Expert-developed QSAR/QSPR ModelsCode0
3D Convolutional Neural Networks for Cross Audio-Visual Matching RecognitionCode0
An online sequence-to-sequence model for noisy speech recognition0
An Overview of Multi-Task Learning in Deep Neural NetworksCode0
Modelling prosodic structure using Artificial Neural Networks0
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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