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 57015750 of 6433 papers

TitleStatusHype
Deep LSTM based Feature Mapping for Query Classification0
An Empirical Study of Automatic Chinese Word Segmentation for Spoken Language Understanding and Named Entity Recognition0
A Joint Model of Orthography and Morphological Segmentation0
Developing language technology tools and resources for a resource-poor language: Sindhi0
Incorporating Side Information into Recurrent Neural Network Language Models0
Assessing Relative Sentence Complexity using an Incremental CCG Parser0
Design and development a children's speech database0
On model architecture for a children's speech recognition interactive dialog system0
Contour-based 3d tongue motion visualization using ultrasound image sequences0
Noisy Parallel Approximate Decoding for Conditional Recurrent Language Model0
The IBM Speaker Recognition System: Recent Advances and Error Analysis0
TheanoLM - An Extensible Toolkit for Neural Network Language Modeling0
A Corpus of Read and Spontaneous Upper Saxon German Speech for ASR Evaluation0
Speech Corpus Spoken by Young-old, Old-old and Oldest-old Japanese0
The SI TEDx-UM speech database: a new Slovenian Spoken Language Resource0
An Extension of the Slovak Broadcast News Corpus based on Semi-Automatic Annotation0
Extracting Weighted Language Lexicons from Wikipedia0
How Diachronic Text Corpora Affect Context based Retrieval of OOV Proper Names for Audio News0
The DIRHA Portuguese Corpus: A Comparison of Home Automation Command Detection and Recognition in Simulated and Real Data.0
Collecting Resources in Sub-Saharan African Languages for Automatic Speech Recognition: a Case Study of WolofCode0
Generating Task-Pertinent sorted Error Lists for Speech Recognition0
Using the TED Talks to Evaluate Spoken Post-editing of Machine Translation0
Challenges and Solutions for Consistent Annotation of Vietnamese Treebank0
Enhanced CORILGA: Introducing the Automatic Phonetic Alignment Tool for Continuous Speech0
Endangered Language Documentation: Bootstrapping a Chatino Speech Corpus, Forced Aligner, ASR0
Falling silent, lost for words ... Tracing personal involvement in interviews with Dutch war veterans0
Designing a Speech Corpus for the Development and Evaluation of Dictation Systems in Latvian0
A Comparative Analysis of Crowdsourced Natural Language Corpora for Spoken Dialog Systems0
Operational Assessment of Keyword Search on Oral History0
The ILMT-s2s Corpus ― A Multimodal Interlingual Map Task Corpus0
Joining-in-type Humanoid Robot Assisted Language Learning System0
Towards Automatic Transcription of ILSE ― an Interdisciplinary Longitudinal Study of Adult Development and Aging0
The IBM 2016 English Conversational Telephone Speech Recognition System0
Speaker Cluster-Based Speaker Adaptive Training for Deep Neural Network Acoustic Modeling0
Composition of Deep and Spiking Neural Networks for Very Low Bit Rate Speech Coding0
Scan, Attend and Read: End-to-End Handwritten Paragraph Recognition with MDLSTM Attention0
Learning Compact Recurrent Neural Networks0
A Survey on Bayesian Deep LearningCode0
Advances in Very Deep Convolutional Neural Networks for LVCSR0
Character-Level Neural Translation for Multilingual Media Monitoring in the SUMMA ProjectCode0
Neural Attention Models for Sequence Classification: Analysis and Application to Key Term Extraction and Dialogue Act Detection0
Learning Multiscale Features Directly From Waveforms0
Differentiable Pooling for Unsupervised Acoustic Model Adaptation0
Model Interpolation with Trans-dimensional Random Field Language Models for Speech Recognition0
On the Compression of Recurrent Neural Networks with an Application to LVCSR acoustic modeling for Embedded Speech Recognition0
A Tutorial on Deep Neural Networks for Intelligent Systems0
Acceleration of Deep Neural Network Training with Resistive Cross-Point Devices0
Neural network based spectral mask estimation for acoustic beamformingCode0
A Comparison between Deep Neural Nets and Kernel Acoustic Models for Speech Recognition0
Personalized Speech recognition on mobile devices0
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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
9Gated ConvNetsWord Error Rate (WER)4.8Unverified
10HMM-TDNN + iVectorsWord 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
7HMM-TDNN + pNorm + speed up/down speechPercentage error12.9Unverified
8DNN MPEPercentage error12.9Unverified
9DNN MMIPercentage error12.9Unverified
10CNN + Bi-RNN + CTC (speech to letters), 25.9% WER if trainedonlyon SWBPercentage 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
6TC-DNN-BLSTM-DNNWord Error Rate (WER)3.5Unverified
7Convolutional Speech RecognitionWord 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