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 53515400 of 6433 papers

TitleStatusHype
Adaptive Dropout for Pruning Conformers0
Adaptive Feature Fusion: Enhancing Generalization in Deep Learning Models0
Adaptive Frequency Cepstral Coefficients for Word Mispronunciation Detection0
Adaptive Multi-Corpora Language Model Training for Speech Recognition0
Adaptive multilingual speech recognition with pretrained models0
Adaptive Parser-Centric Text Normalization0
Adaptive Sparse and Monotonic Attention for Transformer-based Automatic Speech Recognition0
AdaPTwin: Low-Cost Adaptive Compression of Product Twins in Transformers0
AdaScale SGD: A Scale-Invariant Algorithm for Distributed Training0
A Dataset for Multimodal Question Answering in the Cultural Heritage Domain0
AdaTranS: Adapting with Boundary-based Shrinking for End-to-End Speech Translation0
Adding Connectionist Temporal Summarization into Conformer to Improve Its Decoder Efficiency For Speech Recognition0
Additional Shared Decoder on Siamese Multi-view Encoders for Learning Acoustic Word Embeddings0
A Decidability-Based Loss Function0
A Deep Dive into Deep Cluster0
A Deep Generative Acoustic Model for Compositional Automatic Speech Recognition0
A Deep Learning Approach for Similar Languages, Varieties and Dialects0
A deep-learning based native-language classification by using a latent semantic analysis for the NLI Shared Task 20170
A Deep Learning based Wearable Healthcare IoT Device for AI-enabled Hearing Assistance Automation0
A Deep Learning System for Domain-specific Speech Recognition0
A Deliberation-based Joint Acoustic and Text Decoder0
A Demonstration of Dialogue Processing in SimSensei Kiosk0
A Density Ratio Approach to Language Model Fusion in End-To-End Automatic Speech Recognition0
A Differentiable Alignment Framework for Sequence-to-Sequence Modeling via Optimal Transport0
A Digital Neuromorphic Architecture Efficiently Facilitating Complex Synaptic Response Functions Applied to Liquid State Machines0
A Discriminative Entity-Aware Language Model for Virtual Assistants0
A Discriminative Training Procedure for Continuous Translation Models0
A Discussion On the Validity of Manifold Learning0
A distributed cloud-based dialog system for conversational application development0
A Distributed Optimisation Framework Combining Natural Gradient with Hessian-Free for Discriminative Sequence Training0
Adjust-free adversarial example generation in speech recognition using evolutionary multi-objective optimization under black-box condition0
A Domain Adaptation Framework for Speech Recognition Systems with Only Synthetic data0
Adopting Whisper for Confidence Estimation0
A Dual-Decoder Conformer for Multilingual Speech Recognition0
Advanced accent/dialect identification and accentedness assessment with multi-embedding models and automatic speech recognition0
Artificial Intelligence for Cochlear Implants: Review of Strategies, Challenges, and Perspectives0
Advanced Clustering Techniques for Speech Signal Enhancement: A Review and Metanalysis of Fuzzy C-Means, K-Means, and Kernel Fuzzy C-Means Methods0
Advanced Framework for Animal Sound Classification With Features Optimization0
Advanced Long-context End-to-end Speech Recognition Using Context-expanded Transformers0
Advances and Challenges in Deep Lip Reading0
Advances in All-Neural Speech Recognition0
Advances in Very Deep Convolutional Neural Networks for LVCSR0
Advancing Arabic Speech Recognition Through Large-Scale Weakly Supervised Learning0
Advancing Connectionist Temporal Classification With Attention Modeling0
Advancing CTC-CRF Based End-to-End Speech Recognition with Wordpieces and Conformers0
Advancing Hearing Assessment: An ASR-Based Frequency-Specific Speech Test for Diagnosing Presbycusis0
Advancing Momentum Pseudo-Labeling with Conformer and Initialization Strategy0
Advancing Multi-talker ASR Performance with Large Language Models0
Advancing RNN Transducer Technology for Speech Recognition0
Advancing Speech Recognition With No Speech Or With Noisy Speech0
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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