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 62516300 of 6433 papers

TitleStatusHype
A Cross-language Study on Automatic Speech Disfluency Detection0
Text Alignment for Real-Time Crowd Captioning0
Emergence of Gricean Maxims from Multi-Agent Decision Theory0
Differences in User Responses to a Wizard-of-Oz versus Automated System0
MKPLS: Manifold Kernel Partial Least Squares for Lipreading and Speaker Identification0
An Overview of Hindi Speech Recognition0
Opportunities & Challenges In Automatic Speech Recognition0
Automatic Speech Recognition Using Template Model for Man-Machine Interface0
Speech Enhancement Using Pitch Detection Approach For Noisy Environment0
Speech Enhancement Modeling Towards Robust Speech Recognition System0
Techniques for Feature Extraction In Speech Recognition System : A Comparative Study0
CONATION: English Command Input/Output System for Computers0
Analysis of Phonetic Transcription for Danish Automatic Speech Recognition0
Extension of hidden markov model for recognizing large vocabulary of sign language0
Estimating Phoneme Class Conditional Probabilities from Raw Speech Signal using Convolutional Neural Networks0
Speech Recognition with Deep Recurrent Neural NetworksCode0
Human Evaluation of Conceptual Route Graphs for Interpreting Spoken Route Descriptions0
Acoustic, Phonetic and Prosodic Features of Parkinson's disease Speech0
Minimally-Supervised Morphological Segmentation using Adaptor Grammars0
Incremental Tree Substitution Grammar for Parsing and Sentence Prediction0
語音辨識使用統計圖等化方法 (Speech Recognition Leveraging Histogram Equalization Methods) [In Chinese]0
Statistical Method of Building Dialect Language Models for ASR Systems0
Language Modeling for Spoken Dialogue System based on Filtering using Predicate-Argument Structures0
Lattice Rescoring for Speech Recognition using Large Scale Distributed Language Models0
Code-Switch Language Model with Inversion Constraints for Mixed Language Speech Recognition0
Detection of Acoustic-Phonetic Landmarks in Mismatched Conditions using a Biomimetic Model of Human Auditory Processing0
Factored Language Model based on Recurrent Neural Network0
Automatic pronunciation assessment for language learners with acoustic-phonetic features0
Romanized Arabic Transliteration0
A Conditional Random Field-based Traditional Chinese Base Phrase Parser for SIGHAN Bake-off 2012 Evaluation0
Automatic Pronunciation Scoring And Mispronunciation Detection Using CMUSphinx0
Using English Acoustic Models for Hindi Automatic Speech Recognition0
Large Scale Distributed Deep Networks0
Sequence Transduction with Recurrent Neural NetworksCode0
Deep Neural Networks for Acoustic Modeling in Speech Recognition0
Classifying Dialogue Acts in Multi-party Live Chats0
Towards a Semantic Annotation of English Television News - Building and Evaluating a Constraint Grammar FrameNet0
A CRF Sequence Labeling Approach to Chinese Punctuation Prediction0
遞迴式類神經網路語言模型應用額外資訊於語音辨識之研究 (Recurrent Neural Network-based Language Modeling with Extra Information Cues for Speech Recognition) [In Chinese]0
A Possibilistic Approach for Automatic Word Sense Disambiguation0
改良式統計圖等化法強鍵性語音辨識之研究 (Improved Histogram Equalization Methods for Robust Speech Recognition) [In Chinese]0
Incremental Derivations in CCG0
Incremental Neo-Davidsonian semantic construction for TAG0
Finite-State Acoustic and Translation Model Composition in Statistical Speech Translation: Empirical Assessment0
A Methodology for Obtaining Concept Graphs from Word Graphs0
Towards a Self-Learning Assistive Vocal Interface: Vocabulary and Grammar Learning0
WFST-Based Grapheme-to-Phoneme Conversion: Open Source tools for Alignment, Model-Building and Decoding0
A GrAF-compliant Indonesian Speech Recognition Web Service on the Language Grid for Transcription Crowdsourcing0
Reduction of Non-stationary Noise for a Robotic Living Assistant using Sparse Non-negative Matrix Factorization0
Integrating Incremental Speech Recognition and POMDP-Based Dialogue Systems0
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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