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 59516000 of 6433 papers

TitleStatusHype
Maximum a Posteriori Adaptation of Network Parameters in Deep Models0
F0 Modeling In Hmm-Based Speech Synthesis System Using Deep Belief Network0
Hybrid Orthogonal Projection and Estimation (HOPE): A New Framework to Probe and Learn Neural Networks0
Deep Multimodal Learning for Audio-Visual Speech Recognition0
Belief Hidden Markov Model for speech recognition0
Phrase Based Language Model for Statistical Machine Translation: Empirical Study0
Phrase Based Language Model For Statistical Machine Translation0
Preserving Trees in Minimal Automata0
Unsupervised Lexicon Discovery from Acoustic Input0
Fast, simple and accurate handwritten digit classification by training shallow neural network classifiers with the 'extreme learning machine' algorithm0
Learning linearly separable features for speech recognition using convolutional neural networks0
Incremental Adaptation Strategies for Neural Network Language Models0
A Broadcast News Corpus for Evaluation and Tuning of German LVCSR Systems0
End-to-end Continuous Speech Recognition using Attention-based Recurrent NN: First Results0
Retrieval Term Prediction Using Deep Belief Networks0
使用概念資訊於中文大詞彙連續語音辨識之研究 (Exploring Concept Information for Mandarin Large Vocabulary Continuous Speech Recognition) [In Chinese]0
Use of GPU and Feature Reduction for Fast Query-by-Example Spoken Term Detection0
HinMA: Distributed Morphology based Hindi Morphological Analyzer0
Using Tone Information in Thai Spelling Speech Recognition0
Voice Activity Detection using Temporal Characteristics of Autocorrelation Lag and Maximum Spectral Amplitude in Sub-bands0
Zero-Shot Learning of Language Models for Describing Human Actions Based on Semantic Compositionality of Actions0
How to Scale Up Kernel Methods to Be As Good As Deep Neural Nets0
Tied Probabilistic Linear Discriminant Analysis for Speech Recognition0
The Effect of Dependency Representation Scheme on Syntactic Language Modelling0
Parallel training of DNNs with Natural Gradient and Parameter AveragingCode0
Mobility Enhancement for Elderly0
Constructing Long Short-Term Memory based Deep Recurrent Neural Networks for Large Vocabulary Speech Recognition0
Spatial Diffuseness Features for DNN-Based Speech Recognition in Noisy and Reverberant Environments0
Language Identification in Code-Switching Scenario0
Morphological Segmentation for Keyword Spotting0
探究新穎語句模型化技術於節錄式語音摘要 (Investigating Novel Sentence Modeling Techniques for Extractive Speech Summarization) [In Chinese]0
Language Modeling with Functional Head Constraint for Code Switching Speech Recognition0
Combining Punctuation and Disfluency Prediction: An Empirical Study0
Modeling Interestingness with Deep Neural Networks0
運用概念模型化技術於中文大詞彙連續語音辨識之語言模型調適 (Leveraging Concept Modeling Techniques for Language Model Adaptation in Mandarin Large Vocabulary Continuous Speech Recognition) [In Chinese]0
以二維共振峰分布建立語者音色模型及其在語者驗證上之應用 (Using 2D Formant Distribution to Build Speaker Models and Its Application in Speaker Verification) [In Chinese]0
An I-vector Based Approach to Compact Multi-Granularity Topic Spaces Representation of Textual Documents0
Exploration of the Impact of Maximum Entropy in Recurrent Neural Network Language Models for Code-Switching Speech0
Learning Phrase Representations using RNN Encoder--Decoder for Statistical Machine Translation0
Large-scale Reordering Model for Statistical Machine Translation using Dual Multinomial Logistic Regression0
Policy Learning for Domain Selection in an Extensible Multi-domain Spoken Dialogue System0
Leveraging Effective Query Modeling Techniques for Speech Recognition and Summarization0
Visual Words for Automatic Lip-Reading0
Recurrent Neural Network RegularizationCode0
Visual Speech Recognition0
First-Pass Large Vocabulary Continuous Speech Recognition using Bi-Directional Recurrent DNNsCode0
The Effect of Sensor Errors in Situated Human-Computer Dialogue0
Class-Based Language Modeling for Translating into Morphologically Rich Languages0
Key Event Detection in Video using ASR and Visual Data0
Developing further speech recognition resources for Welsh0
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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