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 62016250 of 6433 papers

TitleStatusHype
Recipe For Building Robust Spoken Dialog State Trackers: Dialog State Tracking Challenge System Description0
Which ASR should I choose for my dialogue system?0
homeService: Voice-enabled assistive technology in the home using cloud-based automatic speech recognition0
QuEst - A translation quality estimation framework0
Punctuation Prediction with Transition-based Parsing0
Adaptive Parser-Centric Text Normalization0
A Self Learning Vocal Interface for Speech-impaired Users0
Building bilingual lexicon to create Dialect Tunisian corpora and adapt language model0
Adaptation Data Selection using Neural Language Models: Experiments in Machine Translation0
Making Speech-Based Assistive Technology Work for a Real User0
SLPAT in practice: lessons from translational research0
The Map Task Dialogue System: A Test-bed for Modelling Human-Like Dialogue0
Decipherment0
Predicting Tasks in Goal-Oriented Spoken Dialog Systems using Semantic Knowledge Bases0
A Simple and Generic Belief Tracking Mechanism for the Dialog State Tracking Challenge: On the believability of observed information0
Comparing and combining classifiers for self-taught vocal interfaces0
Multi-domain learning and generalization in dialog state tracking0
Structured Discriminative Model For Dialog State Tracking0
Multi-step Natural Language Understanding0
Analyzing the Performance of Automatic Speech Recognition for Ageing Voice: Does it Correlate with Dependency Level?0
Modelling Human Clarification Strategies0
Individuality-Preserving Voice Conversion for Articulation Disorders Using Locality-Constrained NMF0
Automating speech reception threshold measurements using automatic speech recognition0
Unsupervised structured semantic inference for spoken dialog reservation tasks0
Exploring Features For Localized Detection of Speech Recognition Errors0
The Dialog State Tracking Challenge0
Robust Feature Extraction to Utterance Fluctuation of Articulation Disorders Based on Random Projection0
The Haves and the Have-Nots: Leveraging Unlabelled Corpora for Sentiment Analysis0
WebWOZ: A Platform for Designing and Conducting Web-based Wizard of Oz Experiments0
Modified SPLICE and its Extension to Non-Stereo Data for Noise Robust Speech Recognition0
Computing the Most Probable String with a Probabilistic Finite State Machine0
A Convexity-based Generalization of Viterbi for Non-Deterministic Weighted Automata0
Stochastic Bi-Languages to model Dialogs0
Keyphrase Cloud Generation of Broadcast NewsCode0
Deep Learning using Linear Support Vector MachinesCode0
Text Alignment for Real-Time Crowd Captioning0
User Goal Change Model for Spoken Dialog State Tracking0
A Cross-language Study on Automatic Speech Disfluency Detection0
Distinguishing Common and Proper Nouns0
Semi-Supervised Discriminative Language Modeling with Out-of-Domain Text Data0
MKPLS: Manifold Kernel Partial Least Squares for Lipreading and Speaker Identification0
Applying Unsupervised Learning To Support Vector Space Model Based Speaking Assessment0
Emergence of Gricean Maxims from Multi-Agent Decision Theory0
Differences in User Responses to a Wizard-of-Oz versus Automated System0
Using Out-of-Domain Data for Lexical Addressee Detection in Human-Human-Computer Dialog0
Atypical Prosodic Structure as an Indicator of Reading Level and Text Difficulty0
Experimental Results on the Native Language Identification Shared Task0
Measuring the Structural Importance through Rhetorical Structure Index0
Discriminative Joint Modeling of Lexical Variation and Acoustic Confusion for Automated Narrative Retelling Assessment0
Evaluating Unsupervised Language Model Adaptation Methods for Speaking Assessment0
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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