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

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Showing 5651–5700 of 6433 papers

TitleStatusHype
Joint Word Segmentation and Phonetic Category Induction—0
An Open Web Platform for Rule-Based Speech-to-Sign Translation—0
Off-topic Response Detection for Spontaneous Spoken English Assessment—0
Mining linguistic tone patterns with symbolic representation—0
Cross-lingual projection for class-based language models—0
Improving cross-domain n-gram language modelling with skipgrams—0
Modeling Concept Dependencies in a Scientific Corpus—0
How to Train Dependency Parsers with Inexact Search for Joint Sentence Boundary Detection and Parsing of Entire Documents—0
Blind phoneme segmentation with temporal prediction errors—0
Babler - Data Collection from the Web to Support Speech Recognition and Keyword Search—0
Exponentially Decaying Bag-of-Words Input Features for Feed-Forward Neural Network in Statistical Machine Translation—0
CobaltF: A Fluent Metric for MT Evaluation—0
SHEF-LIUM-NN: Sentence level Quality Estimation with Neural Network Features—0
Detecting Mild Cognitive Impairment by Exploiting Linguistic Information from Transcripts—0
TranscRater: a Tool for Automatic Speech Recognition Quality Estimation—0
Pynini: A Python library for weighted finite-state grammar compilation—0
OpenDial: A Toolkit for Developing Spoken Dialogue Systems with Probabilistic Rules—0
My Science Tutor---Learning Science with a Conversational Virtual Tutor—0
Distributed representation and estimation of WFST-based n-gram models—0
Evaluation of acoustic word embeddings—0
Alignment-Based Neural Machine Translation—0
Unsupervised morph segmentation and statistical language models for vocabulary expansion—0
Trainable Frontend For Robust and Far-Field Keyword SpottingCode0
Piecewise convexity of artificial neural networks—0
DSD: Dense-Sparse-Dense Training for Deep Neural NetworksCode0
On the efficient representation and execution of deep acoustic models—0
Intra-layer Nonuniform Quantization for Deep Convolutional Neural Network—0
Sequence Training and Adaptation of Highway Deep Neural Networks—0
From Human Language Technology to Human Language Science—0
Moving Toward High Precision Dynamical Modelling in Hidden Markov Models—0
Estimation de la qualit\'e d'un syst\`eme de reconnaissance de la parole pour une t\^ache de compr\'ehension (Quality estimation of a Speech Recognition System for a Spoken Language Understanding task)—0
Generation and Pruning of Pronunciation Variants to Improve ASR Accuracy—0
Training LDCRF model on unsegmented sequences using Connectionist Temporal Classification—0
NN-grams: Unifying neural network and n-gram language models for Speech Recognition—0
A segmental framework for fully-unsupervised large-vocabulary speech recognitionCode0
A Comprehensive Study of Deep Bidirectional LSTM RNNs for Acoustic Modeling in Speech Recognition—0
A Curriculum Learning Method for Improved Noise Robustness in Automatic Speech Recognition—0
A Nonparametric Bayesian Approach for Spoken Term detection by Example Query—0
Graph based manifold regularized deep neural networks for automatic speech recognition—0
Increasing the Interpretability of Recurrent Neural Networks Using Hidden Markov Models—0
Spectral decomposition method of dialog state tracking via collective matrix factorization—0
Automatic Pronunciation Generation by Utilizing a Semi-supervised Deep Neural Networks—0
Calibration of Phone Likelihoods in Automatic Speech Recognition—0
Training variance and performance evaluation of neural networks in speech—0
Dialog state tracking, a machine reading approach using Memory Network—0
Distributed Hessian-Free Optimization for Deep Neural Network—0
A Spiking Network that Learns to Extract Spike Signatures from Speech Signals—0
Zara The Supergirl: An Empathetic Personality Recognition System—0
Bidirectional RNN for Medical Event Detection in Electronic Health Records—0
DAG-Structured Long Short-Term Memory for Semantic Compositionality—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