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 59015950 of 6433 papers

TitleStatusHype
Attention-Based Models for Speech RecognitionCode1
Nonparametric Bayesian Double Articulation Analyzer for Direct Language Acquisition from Continuous Speech Signals0
Recognize Foreign Low-Frequency Words with Similar Pairs0
Time Series Classification using the Hidden-Unit Logistic Model0
Scheduled Sampling for Sequence Prediction with Recurrent Neural NetworksCode0
Knowledge Transfer Pre-training0
Hybridized Feature Extraction and Acoustic Modelling Approach for Dysarthric Speech Recognition0
Beyond Temporal Pooling: Recurrence and Temporal Convolutions for Gesture Recognition in VideoCode0
Towards Structured Deep Neural Network for Automatic Speech Recognition0
Learning Speech Rate in Speech Recognition0
Unsupervised Text Normalization Using Distributed Representations of Words and Phrases0
Scaling Semantic Frame Annotation0
SOPA: Random Forests Regression for the Semantic Textual Similarity task0
EL92: Entity Linking Combining Open Source Annotators via Weighted Voting0
Combining Open Source Annotators for Entity Linking through Weighted Voting0
Cache-Augmented Latent Topic Language Models for Speech Retrieval0
A Web Application for Automated Dialect Analysis0
Book Reviews: Robots that Talk and Listen edited by Judith A. Markowitz0
Open Ended Intelligence: The individuation of Intelligent Agents0
The IBM 2015 English Conversational Telephone Speech Recognition System0
Recurrent Neural Network Training with Dark Knowledge Transfer0
Feature selection using Fisher's ratio technique for automatic speech recognition0
Improving neural networks with bunches of neurons modeled by Kumaraswamy units: Preliminary study0
Smart Computer Aided Translation Environment - SCATE0
Using sub-word n-gram models for dealing with OOV in large vocabulary speech recognition for Latvian0
Assessing the Performance of Automatic Speech Recognition Systems When Used by Native and Non-Native Speakers of Three Major Languages in Dictation Workflows0
Sentence Compression For Automatic Subtitling0
Deep Learning and Continuous Representations for Natural Language Processing0
Unediting: Detecting Disfluencies Without Careful Transcripts0
Semantic parsing of speech using grammars learned with weak supervision0
Entity Linking for Spoken Language0
Sentence segmentation of aphasic speech0
Lexicon-Free Conversational Speech Recognition with Neural Networks0
Multitask Learning for Adaptive Quality Estimation of Automatically Transcribed Utterances0
When and why are log-linear models self-normalizing?0
An Incremental Algorithm for Transition-based CCG Parsing0
Convolutional Neural Network for Paraphrase IdentificationCode0
A Probabilistic Framework for Representing Dialog Systems and Entropy-Based Dialog Management through Dynamic Stochastic State Evolution0
Voice based self help System: User Experience Vs Accuracy0
Transferring Knowledge from a RNN to a DNN0
Deep Recurrent Neural Networks for Acoustic Modelling0
A Simple Way to Initialize Recurrent Networks of Rectified Linear UnitsCode0
A Probabilistic Theory of Deep LearningCode0
Gibbs Sampling with Low-Power Spiking Digital Neurons0
Video-Based Action Recognition Using Rate-Invariant Analysis of Covariance Trajectories0
Long Short-Term Memory Over Tree Structures0
LSTM: A Search Space OdysseyCode0
Modeling State-Conditional Observation Distribution using Weighted Stereo Samples for Factorial Speech Processing Models0
Maximum a Posteriori Adaptation of Network Parameters in Deep Models0
F0 Modeling In Hmm-Based Speech Synthesis System Using Deep Belief Network0
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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