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 24012450 of 6433 papers

TitleStatusHype
A Short Survey on Data Clustering Algorithms0
A Generalized Framework for Hierarchical Word Sequence Language Model0
Compacting Neural Network Classifiers via Dropout Training0
Compact, Efficient and Unlimited Capacity: Language Modeling with Compressed Suffix Trees0
A Semi-autonomous System for Creating a Human-Machine Interaction Corpus in Virtual Reality: Application to the ACORFORMed System for Training Doctors to Break Bad News0
Communication strategies for a computerized caregiver for individuals with Alzheimer's disease0
Communication-Efficient Personalized Federated Learning for Speech-to-Text Tasks0
A Semi-Automated Live Interlingual Communication Workflow Featuring Intralingual Respeaking: Evaluation and Benchmarking0
A GEN AI Framework for Medical Note Generation0
A Semantic Analyzer for the Comprehension of the Spontaneous Arabic Speech0
CommonAccent: Exploring Large Acoustic Pretrained Models for Accent Classification Based on Common Voice0
CommanderSong: A Systematic Approach for Practical Adversarial Voice Recognition0
A Self Learning Vocal Interface for Speech-impaired Users0
AGADIR: Towards Array-Geometry Agnostic Directional Speech Recognition0
Combining X-Vectors and Bayesian Batch Active Learning: Two-Stage Active Learning Pipeline for Speech Recognition0
Combining Unsupervised and Text Augmented Semi-Supervised Learning for Low Resourced Autoregressive Speech Recognition0
Combining Spectral and Self-Supervised Features for Low Resource Speech Recognition and Translation0
A Self-Attentive Model with Gate Mechanism for Spoken Language Understanding0
Acoustic Model Fusion for End-to-end Speech Recognition0
Accent-Robust Automatic Speech Recognition Using Supervised and Unsupervised Wav2vec Embeddings0
Combining Punctuation and Disfluency Prediction: An Empirical Study0
Combining Open Source Annotators for Entity Linking through Weighted Voting0
Combining Natural Gradient with Hessian Free Methods for Sequence Training0
A Scalable Architecture For Web Deployment of Spoken Dialogue Systems0
Combining Multiple Views for Visual Speech Recognition0
Combining Language Models For Specialized Domains: A Colorful Approach0
AS-ASR: A Lightweight Framework for Aphasia-Specific Automatic Speech Recognition0
Combining Human Inputters and Language Services to provide Multi-language support system for International Symposiums0
ASAPP-ASR: Multistream CNN and Self-Attentive SRU for SOTA Speech Recognition0
Afrispeech-Dialog: A Benchmark Dataset for Spontaneous English Conversations in Healthcare and Beyond0
Acoustic Model Compression with MAP adaptation0
Combination of Recurrent Neural Networks and Factored Language Models for Code-Switching Language Modeling0
AS-70: A Mandarin stuttered speech dataset for automatic speech recognition and stuttering event detection0
Collection and Analysis of Code-switch Egyptian Arabic-English Speech Corpus0
AfriSpeech-200: Pan-African Accented Speech Dataset for Clinical and General Domain ASR0
Collecting Code-Switched Data from Social Media0
CoLLD: Contrastive Layer-to-layer Distillation for Compressing Multilingual Pre-trained Speech Encoders0
ArzEn: A Speech Corpus for Code-switched Egyptian Arabic-English0
Collaborative Training of Acoustic Encoders for Speech Recognition0
Collaborative Learning with Artificial Intelligence Speakers (CLAIS): Pre-Service Elementary Science Teachers' Responses to the Prototype0
AfriNames: Most ASR models "butcher" African Names0
Acoustic Feature Mixup for Balanced Multi-aspect Pronunciation Assessment0
Accent Recognition with Hybrid Phonetic Features0
3D Feature Pyramid Attention Module for Robust Visual Speech Recognition0
Collaborative Data Relabeling for Robust and Diverse Voice Apps Recommendation in Intelligent Personal Assistants0
Co-learning synaptic delays, weights and adaptation in spiking neural networks0
Artificial Neural Networks to Recognize Speakers Division from Continuous Bengali Speech0
Cold Fusion: Training Seq2Seq Models Together with Language Models0
Artie Bias Corpus: An Open Dataset for Detecting Demographic Bias in Speech Applications0
Code-Switch Language Model with Inversion Constraints for Mixed Language Speech Recognition0
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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
9Gated ConvNetsWord Error Rate (WER)4.8Unverified
10HMM-TDNN + iVectorsWord 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
7HMM-TDNN + pNorm + speed up/down speechPercentage error12.9Unverified
8DNN MPEPercentage error12.9Unverified
9DNN MMIPercentage error12.9Unverified
10CNN + Bi-RNN + CTC (speech to letters), 25.9% WER if trainedonlyon SWBPercentage 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
6TC-DNN-BLSTM-DNNWord Error Rate (WER)3.5Unverified
7Convolutional Speech RecognitionWord 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