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 39514000 of 6433 papers

TitleStatusHype
AVLnet: Learning Audio-Visual Language Representations from Instructional VideosCode1
Towards Automated Assessment of Stuttering and Stuttering Therapy0
End-to-End Code Switching Language Models for Automatic Speech Recognition0
Exploration of End-to-End ASR for OpenSTT -- Russian Open Speech-to-Text Dataset0
Regularized Forward-Backward Decoder for Attention Models0
Emotion Recognition in Audio and Video Using Deep Neural NetworksCode1
The JHU Multi-Microphone Multi-Speaker ASR System for the CHiME-6 Challenge0
UWSpeech: Speech to Speech Translation for Unwritten Languages0
"Notic My Speech" -- Blending Speech Patterns With Multimedia0
FrugalML: How to Use ML Prediction APIs More Accurately and Cheaply0
Evaluation of Neural Architectures Trained with Square Loss vs Cross-Entropy in Classification TasksCode0
Anti-Transfer Learning for Task Invariance in Convolutional Neural Networks for Speech ProcessingCode0
Data Augmentation for Training Dialog Models Robust to Speech Recognition Errors0
Learning not to Discriminate: Task Agnostic Learning for Improving Monolingual and Code-switched Speech Recognition0
audino: A Modern Annotation Tool for Audio and SpeechCode2
On the Effectiveness of Neural Text Generation based Data Augmentation for Recognition of Morphologically Rich Speech0
Improving Cross-Lingual Transfer Learning for End-to-End Speech Recognition with Speech Translation0
Learning to Count Words in Fluent Speech enables Online Speech RecognitionCode1
Contextual RNN-T For Open Domain ASR0
Multi-talker ASR for an unknown number of sources: Joint training of source counting, separation and ASR0
Characterizing the Weight Space for Different Learning Models0
Self-Training for End-to-End Speech Translation0
Transfer Learning for British Sign Language Modelling0
Dilated U-net based approach for multichannel speech enhancement from First-Order Ambisonics recordings0
Improved acoustic word embeddings for zero-resource languages using multilingual transferCode1
Analyzing the Quality and Stability of a Streaming End-to-End On-Device Speech Recognizer0
Surprisal-Triggered Conditional Computation with Neural NetworksCode0
PolyDL: Polyhedral Optimizations for Creation of High Performance DL primitivesCode1
Detecting Audio Attacks on ASR Systems with Dropout Uncertainty0
Introduction d'informations s\'emantiques dans un syst\`eme de reconnaissance de la parole (Despite spectacular advances in recent years, the Automatic Speech Recognition (ASR) systems still make mistakes, especially in noisy environments)0
Reconnaissance automatique de la parole : g\'en\'eration des prononciations non natives pour l'enrichissement du lexique (In this study we propose a method for lexicon adaptation in order to improve the automatic speech recognition (ASR) of non-native speakers)0
Sur l'utilisation de la reconnaissance automatique de la parole pour l'aide au diagnostic diff\'erentiel entre la maladie de Parkinson et l'AMS (On using automatic speech recognition for the differential diagnosis of Parkinson's Disease and MSA This article presents a study regarding the contribution of automatic speech processing in the differential diagnosis between Parkinson's disease and MSA (Multi-System Atrophies))0
Analyse de l'effet de la r\'everb\'eration sur la reconnaissance automatique de la parole (Analyzing how reverberation affects Automatic Speech Recognition)0
Constrained Variational Autoencoder for improving EEG based Speech Recognition Systems0
An Effective Contextual Language Modeling Framework for Speech Summarization with Augmented Features0
Learning to Recognize Code-switched Speech Without Forgetting Monolingual Speech Recognition0
Streaming Language Identification using Combination of Acoustic Representations and ASR Hypotheses0
Dynamic Masking for Improved Stability in Spoken Language Translation0
Improving EEG based continuous speech recognition using GAN0
Understanding effect of speech perception in EEG based speech recognition systems0
Improving Unsupervised Sparsespeech Acoustic Models with Categorical ReparameterizationCode0
Adversarial Attacks and Defense on Texts: A Survey0
Subword RNNLM Approximations for Out-Of-Vocabulary Keyword SearchCode1
When Can Self-Attention Be Replaced by Feed Forward Layers?0
On the Comparison of Popular End-to-End Models for Large Scale Speech RecognitionCode1
Phone Features Improve Speech TranslationCode0
CAT: A CTC-CRF based ASR Toolkit Bridging the Hybrid and the End-to-end Approaches towards Data Efficiency and Low Latency0
Predicting Entity Popularity to Improve Spoken Entity Recognition by Virtual Assistants0
FT Speech: Danish Parliament Speech Corpus0
An Audio-enriched BERT-based Framework for Spoken Multiple-choice Question Answering0
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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