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 44514500 of 6433 papers

TitleStatusHype
探究端對端語音辨識於發音檢測與診斷(Investigating on Computer-Assisted Pronunciation Training Leveraging End-to-End Speech Recognition Techniques)0
Targeted Adversarial Examples for Black Box Audio Systems0
TASK AWARE MULTI-TASK LEARNING FOR SPEECH TO TEXT TASKS0
Task-aware Warping Factors in Mask-based Speech Enhancement0
Task-dependent modulation of the visual sensory thalamus assists visual-speech recognition0
Task Lineages: Dialog State Tracking for Flexible Interaction0
Task-oriented Document-Grounded Dialog Systems by HLTPR@RWTH for DSTC9 and DSTC100
Task-Specific Audio Coding for Machines: Machine-Learned Latent Features Are Codes for That Machine0
TBD: Benchmarking and Analyzing Deep Neural Network Training0
Teach an all-rounder with experts in different domains0
Team Deep Mixture of Experts for Distributed Power Control0
Team MTS @ AutoMin 2021: An Overview of Existing Summarization Approaches and Comparison to Unsupervised Summarization Techniques0
Techniques for Feature Extraction In Speech Recognition System : A Comparative Study0
Techniques for Vocabulary Expansion in Hybrid Speech Recognition Systems0
Technology-Augmented Multilingual Communication Models: New Interaction Paradigms, Shifts in the Language Services Industry, and Implications for Training Programs0
TED-LIUM: an Automatic Speech Recognition dedicated corpus0
Temperature-Based Deep Boltzmann Machines0
Temporal Attention Augmented Transformer Hawkes Process0
Temporal Information Processing on Noisy Quantum Computers0
Temporal Multimodal Learning in Audiovisual Speech Recognition0
Temporal Order Preserved Optimal Transport-based Cross-modal Knowledge Transfer Learning for ASR0
Tencent-MVSE: A Large-Scale Benchmark Dataset for Multi-Modal Video Similarity Evaluation0
Tensor decomposition for minimization of E2E SLU model toward on-device processing0
Teochew-Wild: The First In-the-wild Teochew Dataset with Orthographic Annotations0
Text Alignment for Real-Time Crowd Captioning0
Text-based Speaker Identification on Multiparty Dialogues Using Multi-document Convolutional Neural Networks0
Text Generation with Speech Synthesis for ASR Data Augmentation0
Text Injection for Capitalization and Turn-Taking Prediction in Speech Models0
Text Injection for Neural Contextual Biasing0
Text is All You Need: Personalizing ASR Models using Controllable Speech Synthesis0
Text Normalization Infrastructure that Scales to Hundreds of Language Varieties0
Text-only domain adaptation for end-to-end ASR using integrated text-to-mel-spectrogram generator0
Text-Only Domain Adaptation for End-to-End Speech Recognition through Down-Sampling Acoustic Representation0
Text-only Domain Adaptation using Unified Speech-Text Representation in Transducer0
Text-To-Speech Data Augmentation for Low Resource Speech Recognition0
Textual Echo Cancellation0
Textual Inference and Meaning Representation in Human Robot Interaction0
Thank you for Attention: A survey on Attention-based Artificial Neural Networks for Automatic Speech Recognition0
That Sounds Familiar: an Analysis of Phonetic Representations Transfer Across Languages0
The 2015 Sheffield System for Transcription of Multi-Genre Broadcast Media0
The 2019 BBN Cross-lingual Information Retrieval System0
The acquisition and dialog act labeling of the EDECAN-SPORTS corpus0
The AFRL IWSLT 2018 Systems: What Worked, What Didn’t0
The AFRL IWSLT 2020 Systems: Work-From-Home Edition0
The Algonauts Project: A Platform for Communication between the Sciences of Biological and Artificial Intelligence0
The Ambiguous World of Emotion Representation0
TheanoLM - An Extensible Toolkit for Neural Network Language Modeling0
The Art of Deception: Robust Backdoor Attack using Dynamic Stacking of Triggers0
The ASRU 2019 Mandarin-English Code-Switching Speech Recognition Challenge: Open Datasets, Tracks, Methods and Results0
The aurora experimental framework for the performance evaluation of speech recognition systems under noisy conditions0
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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