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.

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Papers

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Showing 6251–6300 of 6433 papers

TitleStatusHype
Segmentation Strategies for Streaming Speech Translation—0
Grouping Language Model Boundary Words to Speed K--Best Extraction from Hypergraphs—0
Driven Decoding for machine translation (Vers un d\'ecodage guid\'e pour la traduction automatique) [in French]—0
Prompt-based Content Scoring for Automated Spoken Language Assessment—0
Optimal Data Set Selection: An Application to Grapheme-to-Phoneme Conversion—0
Speech Enhancement Using Pitch Detection Approach For Noisy Environment—0
Opportunities & Challenges In Automatic Speech Recognition—0
Automatic Speech Recognition Using Template Model for Man-Machine Interface—0
An Overview of Hindi Speech Recognition—0
Speech Enhancement Modeling Towards Robust Speech Recognition System—0
Techniques for Feature Extraction In Speech Recognition System : A Comparative Study—0
CONATION: English Command Input/Output System for Computers—0
Analysis of Phonetic Transcription for Danish Automatic Speech Recognition—0
Extension of hidden markov model for recognizing large vocabulary of sign language—0
Estimating Phoneme Class Conditional Probabilities from Raw Speech Signal using Convolutional Neural Networks—0
Speech Recognition with Deep Recurrent Neural NetworksCode0
Human Evaluation of Conceptual Route Graphs for Interpreting Spoken Route Descriptions—0
Minimally-Supervised Morphological Segmentation using Adaptor Grammars—0
Incremental Tree Substitution Grammar for Parsing and Sentence Prediction—0
Acoustic, Phonetic and Prosodic Features of Parkinson's disease Speech—0
Using English Acoustic Models for Hindi Automatic Speech Recognition—0
Large Scale Distributed Deep Networks—0
Factored Language Model based on Recurrent Neural Network—0
Romanized Arabic Transliteration—0
Lattice Rescoring for Speech Recognition using Large Scale Distributed Language Models—0
Code-Switch Language Model with Inversion Constraints for Mixed Language Speech Recognition—0
Language Modeling for Spoken Dialogue System based on Filtering using Predicate-Argument Structures—0
Statistical Method of Building Dialect Language Models for ASR Systems—0
Detection of Acoustic-Phonetic Landmarks in Mismatched Conditions using a Biomimetic Model of Human Auditory Processing—0
Automatic Pronunciation Scoring And Mispronunciation Detection Using CMUSphinx—0
Automatic pronunciation assessment for language learners with acoustic-phonetic features—0
語音辨識使用統計圖等化方法 (Speech Recognition Leveraging Histogram Equalization Methods) [In Chinese]—0
A Conditional Random Field-based Traditional Chinese Base Phrase Parser for SIGHAN Bake-off 2012 Evaluation—0
Sequence Transduction with Recurrent Neural NetworksCode0
Deep Neural Networks for Acoustic Modeling in Speech Recognition—0
Classifying Dialogue Acts in Multi-party Live Chats—0
A CRF Sequence Labeling Approach to Chinese Punctuation Prediction—0
Towards a Semantic Annotation of English Television News - Building and Evaluating a Constraint Grammar FrameNet—0
Incremental Neo-Davidsonian semantic construction for TAG—0
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
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
A Possibilistic Approach for Automatic Word Sense Disambiguation—0
Incremental Derivations in CCG—0
IRIS: a Chat-oriented Dialogue System based on the Vector Space Model—0
The Effect of Cognitive Load on a Statistical Dialogue System—0
Assessment of ESL Learners' Syntactic Competence Based on Similarity Measures—0
Probabilistic Dialogue Models with Prior Domain Knowledge—0
Universal Grapheme-to-Phoneme Prediction Over Latin Alphabets—0
Mixing Multiple Translation Models in Statistical Machine Translation—0
Fast and Scalable Decoding with Language Model Look-Ahead for Phrase-based Statistical Machine Translation—0
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All datasetsLibriSpeech test-cleanLibriSpeech test-otherSwitchboard + Hub500TIMITAISHELL-1WSJ eval92Common Voice Germanswb_hub_500 WER fullSWBCHTUDACommon Voice FrenchCommon Voice SpanishMediaSpeech

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AmNetWord Error Rate (WER)8.6—Unverified
2HMM-(SAT)GMMWord Error Rate (WER)8—Unverified
3Local Prior Matching (Large Model)Word Error Rate (WER)7.19—Unverified
4SnipsWord Error Rate (WER)6.4—Unverified
5Li-GRUWord Error Rate (WER)6.2—Unverified
6HMM-DNN + pNorm*Word Error Rate (WER)5.5—Unverified
7CTC + policy learningWord Error Rate (WER)5.42—Unverified
8Deep Speech 2Word Error Rate (WER)5.33—Unverified
9HMM-TDNN + iVectorsWord Error Rate (WER)4.8—Unverified
10Gated ConvNetsWord Error Rate (WER)4.8—Unverified
#ModelMetricClaimedVerifiedStatus
1Local Prior Matching (Large Model)Word Error Rate (WER)20.84—Unverified
2SnipsWord Error Rate (WER)16.5—Unverified
3Local Prior Matching (Large Model, ConvLM LM)Word Error Rate (WER)15.28—Unverified
4Deep Speech 2Word Error Rate (WER)13.25—Unverified
5TDNN + pNorm + speed up/down speechWord Error Rate (WER)12.5—Unverified
6CTC-CRF 4gram-LMWord Error Rate (WER)10.65—Unverified
7Convolutional Speech RecognitionWord Error Rate (WER)10.47—Unverified
8MT4SSLWord Error Rate (WER)9.6—Unverified
9Jasper DR 10x5Word Error Rate (WER)8.79—Unverified
10EspressoWord Error Rate (WER)8.7—Unverified
#ModelMetricClaimedVerifiedStatus
1Deep SpeechPercentage error20—Unverified
2DNN-HMMPercentage error18.5—Unverified
3CD-DNNPercentage error16.1—Unverified
4DNNPercentage error16—Unverified
5DNN + DropoutPercentage error15—Unverified
6DNN BMMIPercentage error12.9—Unverified
7DNN MPEPercentage error12.9—Unverified
8DNN MMIPercentage error12.9—Unverified
9HMM-TDNN + pNorm + speed up/down speechPercentage error12.9—Unverified
10HMM-DNN +sMBRPercentage error12.6—Unverified
#ModelMetricClaimedVerifiedStatus
1LSNNPercentage error33.2—Unverified
2LAS multitask with indicators samplingPercentage error20.4—Unverified
3Soft Monotonic Attention (ours, offline)Percentage error20.1—Unverified
4QCNN-10L-256FMPercentage error19.64—Unverified
5Bi-LSTM + skip connections w/ CTCPercentage error17.7—Unverified
6Bi-RNN + AttentionPercentage error17.6—Unverified
7RNN-CRF on 24(x3) MFSCPercentage error17.3—Unverified
8CNN in time and frequency + dropout, 17.6% w/o dropoutPercentage error16.7—Unverified
9Light Gated Recurrent UnitsPercentage error16.7—Unverified
10GRUPercentage error16.6—Unverified
#ModelMetricClaimedVerifiedStatus
1AttWord Error Rate (WER)18.7—Unverified
2CTC/AttWord Error Rate (WER)6.7—Unverified
3BRA-EWord Error Rate (WER)6.63—Unverified
4CTC-CRF 4gram-LMWord Error Rate (WER)6.34—Unverified
5BATWord Error Rate (WER)4.97—Unverified
6ParaformerWord Error Rate (WER)4.95—Unverified
7U2Word Error Rate (WER)4.72—Unverified
8UMAWord Error Rate (WER)4.7—Unverified
9Lightweight TransducerWord Error Rate (WER)4.31—Unverified
10CIF-HKD With LMWord Error Rate (WER)4.1—Unverified
#ModelMetricClaimedVerifiedStatus
1Jasper 10x3Word Error Rate (WER)6.9—Unverified
2CNN over RAW speech (wav)Word Error Rate (WER)5.6—Unverified
3CTC-CRF 4gram-LMWord Error Rate (WER)3.79—Unverified
4Deep Speech 2Word Error Rate (WER)3.6—Unverified
5test-set on open vocabulary (i.e. harder), model = HMM-DNN + pNorm*Word Error Rate (WER)3.6—Unverified
6Convolutional Speech RecognitionWord Error Rate (WER)3.5—Unverified
7TC-DNN-BLSTM-DNNWord Error Rate (WER)3.5—Unverified
8EspressoWord Error Rate (WER)3.4—Unverified
9CTC-CRF VGG-BLSTMWord Error Rate (WER)3.2—Unverified
10Transformer with Relaxed AttentionWord Error Rate (WER)3.19—Unverified