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 51015150 of 6433 papers

TitleStatusHype
The WAW Corpus: The First Corpus of Interpreted Speeches and their Translations for English and Arabic0
Contextual Dependencies in Time-Continuous Multidimensional Affect Recognition0
Towards an Automatic Assessment of Crowdsourced Data for NLU0
Neural Caption Generation for News Images0
An Application for Building a Polish Telephone Speech Corpus0
Matics Software Suite: New Tools for Evaluation and Data Exploration0
A Comparative Study of Extremely Low-Resource Transliteration of the World's Languages0
Building Open Javanese and Sundanese Corpora for Multilingual Text-to-Speech0
CPJD Corpus: Crowdsourced Parallel Speech Corpus of Japanese Dialects0
Towards Processing of the Oral History Interviews and Related Printed Documents0
Collecting Code-Switched Data from Social Media0
MOCCA: Measure of Confidence for Corpus Analysis - Automatic Reliability Check of Transcript and Automatic Segmentation0
Automatic Documentation of ICD Codes with Far-Field Speech Recognition0
Investigations on End-to-End Audiovisual Fusion0
Syllable-Based Sequence-to-Sequence Speech Recognition with the Transformer in Mandarin ChineseCode0
Optimus: An Efficient Dynamic Resource Scheduler for Deep Learning ClustersCode0
Sparse Persistent RNNs: Squeezing Large Recurrent Networks On-Chip0
End-to-End Multimodal Speech Recognition0
Recent Progresses in Deep Learning based Acoustic Models (Updated)0
Automatic speech recognition for launch control center communication using recurrent neural networks with data augmentation and custom language model0
An Information-Theoretic View for Deep Learning0
Multi-Head Decoder for End-to-End Speech Recognition0
Precise Detection of Speech Endpoints Dynamically: A Wavelet Convolution based approach0
Neural Compatibility Modeling with Attentive Knowledge Distillation0
Neural Network Language Modeling with Letter-based Features and Importance Sampling0
Twin Regularization for online speech recognitionCode0
Language Recognition using Time Delay Deep Neural Network0
Global SNR Estimation of Speech Signals using Entropy and Uncertainty Estimates from Dropout Networks0
Vision as an Interlingua: Learning Multilingual Semantic Embeddings of Untranscribed Speech0
Speech Commands: A Dataset for Limited-Vocabulary Speech RecognitionCode1
Scalable Factorized Hierarchical Variational Autoencoder TrainingCode0
Attentive Sequence-to-Sequence Learning for Diacritic Restoration of Yorùbá Language TextCode1
Spoken SQuAD: A Study of Mitigating the Impact of Speech Recognition Errors on Listening ComprehensionCode1
Joint Learning of Interactive Spoken Content Retrieval and Trainable User Simulator0
ESPnet: End-to-End Speech Processing Toolkit0
Towards Unsupervised Automatic Speech Recognition Trained by Unaligned Speech and Text only0
The fifth 'CHiME' Speech Separation and Recognition Challenge: Dataset, task and baselines0
Machine Speech Chain with One-shot Speaker Adaptation0
Student-Teacher Learning for BLSTM Mask-based Speech Enhancement0
Building state-of-the-art distant speech recognition using the CHiME-4 challenge with a setup of speech enhancement baseline0
Comprehending Real Numbers: Development of Bengali Real Number Speech Corpus0
A Multi-Discriminator CycleGAN for Unsupervised Non-Parallel Speech Domain Adaptation0
Investigating Generative Adversarial Networks based Speech Dereverberation for Robust Speech RecognitionCode0
Multi-Modal Data Augmentation for End-to-End ASR0
Light Gated Recurrent Units for Speech RecognitionCode0
Spectral feature mapping with mimic loss for robust speech recognition0
Clipping free attacks against artificial neural networks0
Long short-term memory and learning-to-learn in networks of spiking neuronsCode0
Low-Resource Speech-to-Text Translation0
Exploring the robustness of features and enhancement on speech recognition systems in highly-reverberant real environments0
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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