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 49515000 of 6433 papers

TitleStatusHype
Unsupervised Domain Adaptation by Adversarial Learning for Robust Speech Recognition0
Code-Switching Detection with Data-Augmented Acoustic and Language Models0
Acoustic and Textual Data Augmentation for Improved ASR of Code-Switching Speech0
Building a Unified Code-Switching ASR System for South African Languages0
Back-Translation-Style Data Augmentation for End-to-End ASR0
Articulatory Features for ASR of Pathological Speech0
A Comparison of Techniques for Language Model Integration in Encoder-Decoder Speech RecognitionCode0
Open Source Automatic Speech Recognition for GermanCode1
Acoustic-to-Word Recognition with Sequence-to-Sequence Models0
Zero-shot keyword spotting for visual speech recognition in-the-wildCode1
Automatic Speech Recognition for Humanitarian Applications in Somali0
NullaNet: Training Deep Neural Networks for Reduced-Memory-Access Inference0
Multi-scale Alignment and Contextual History for Attention Mechanism in Sequence-to-sequence Model0
Spatial Correlation and Value Prediction in Convolutional Neural Networks0
Hierarchical Multi Task Learning With CTC0
Learning Noise-Invariant Representations for Robust Speech Recognition0
Training Recurrent Neural Networks against Noisy Computations during Inference0
Hierarchical Multitask Learning for CTC-based Speech Recognition0
Concept-Based Embeddings for Natural Language Processing0
Syllabification by Phone Categorization0
Hybrid CTC-Attention based End-to-End Speech Recognition using Subword Units0
Large-Scale Visual Speech Recognition0
A Comparison of Adaptation Techniques and Recurrent Neural Network ArchitecturesCode0
A Fast-Converged Acoustic Modeling for Korean Speech Recognition: A Preliminary Study on Time Delay Neural Network0
Big-Little Net: An Efficient Multi-Scale Feature Representation for Visual and Speech RecognitionCode0
Foreign English Accent Adjustment by Learning Phonetic Patterns0
On Training Recurrent Networks with Truncated Backpropagation Through Time in Speech Recognition0
Improving Deep Learning through Automatic Programming0
Learning The Sequential Temporal Information with Recurrent Neural Networks0
Neural Language Codes for Multilingual Acoustic Models0
Exploring End-to-End Techniques for Low-Resource Speech Recognition0
Weight-importance sparse training in keyword spotting0
A Unified Neural Architecture for Joint Dialog Act Segmentation and Recognition in Spoken Dialog System0
Estimating User Interest from Open-Domain Dialogue0
A Bilingual Interactive Human Avatar Dialogue System0
Joint Part-of-Speech and Language ID Tagging for Code-Switched Data0
Language Informed Modeling of Code-Switched Text0
Low-Resource Machine Transliteration Using Recurrent Neural Networks of Asian Languages0
The Importance of Recommender and Feedback Features in a Pronunciation Learning Aid0
Phone Merging For Code-Switched Speech Recognition0
OpenSeq2Seq: Extensible Toolkit for Distributed and Mixed Precision Training of Sequence-to-Sequence Models0
Transliteration Better than Translation? Answering Code-mixed Questions over a Knowledge Base0
Integrating Multiple NLP Technologies into an Open-source Platform for Multilingual Media Monitoring0
Automatic Detection of Code-switching Style from Acoustics0
Sentiment Analysis using Imperfect Views from Spoken Language and Acoustic Modalities0
Word Error Rate Estimation for Speech Recognition: e-WERCode1
Global-Locally Self-Attentive Encoder for Dialogue State Tracking0
Language Modeling for Code-Mixing: The Role of Linguistic Theory based Synthetic Data0
Improving Slot Filling in Spoken Language Understanding with Joint Pointer and AttentionCode0
Augmented Cyclic Adversarial Learning for Low Resource Domain AdaptationCode0
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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