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 35513600 of 6433 papers

TitleStatusHype
Transformer-Transducers for Code-Switched Speech Recognition0
Transformer with Bidirectional Decoder for Speech Recognition0
Transforming NLU with Babylon: A Case Study in Development of Real-time, Edge-Efficient, Multi-Intent Translation System for Automated Drive-Thru Ordering0
TransfoRNN: Capturing the Sequential Information in Self-Attention Representations for Language Modeling0
Transgressing the boundaries: towards a rigorous understanding of deep learning and its (non-)robustness0
Transliterated Mobile Keyboard Input via Weighted Finite-State Transducers0
Transliterated Zero-Shot Domain Adaptation for Automatic Speech Recognition0
Transliteration Better than Translation? Answering Code-mixed Questions over a Knowledge Base0
Transsion TSUP's speech recognition system for ASRU 2023 MADASR Challenge0
TranUSR: Phoneme-to-word Transcoder Based Unified Speech Representation Learning for Cross-lingual Speech Recognition0
Tree-constrained Pointer Generator for End-to-end Contextual Speech Recognition0
Tree-constrained Pointer Generator with Graph Neural Network Encodings for Contextual Speech Recognition0
Tricks from Deep Learning0
Tropical Modeling of Weighted Transducer Algorithms on Graphs0
TRScore: A Novel GPT-based Readability Scorer for ASR Segmentation and Punctuation model evaluation and selection0
Trustera: A Live Conversation Redaction System0
TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks0
t-SOT FNT: Streaming Multi-talker ASR with Text-only Domain Adaptation Capability0
TSSR: A Truncated and Signed Square Root Activation Function for Neural Networks0
TTS Skins: Speaker Conversion via ASR0
TTS-Transducer: End-to-End Speech Synthesis with Neural Transducer0
TUKE-BNews-SK: Slovak Broadcast News Corpus Construction and Evaluation0
Turkish Resources for Visual Word Recognition0
Turn-taking phenomena in incremental dialogue systems0
Turn-Taking Prediction for Natural Conversational Speech0
Tutorial Proposal: End-to-End Speech Translation0
TutorNet: Towards Flexible Knowledge Distillation for End-to-End Speech Recognition0
Two Front-Ends, One Model : Fusing Heterogeneous Speech Features for Low Resource ASR with Multilingual Pre-Training0
Two-pass Decoding and Cross-adaptation Based System Combination of End-to-end Conformer and Hybrid TDNN ASR Systems0
Two-pass Endpoint Detection for Speech Recognition0
Two-Pass End-to-End ASR Model Compression0
Two-Pass Low Latency End-to-End Spoken Language Understanding0
Two-Stage Augmentation and Adaptive CTC Fusion for Improved Robustness of Multi-Stream End-to-End ASR0
Two-Staged Acoustic Modeling Adaption for Robust Speech Recognition by the Example of German Oral History Interviews0
A Multi-level Acoustic Feature Extraction Framework for Transformer Based End-to-End Speech Recognition0
U2++ MoE: Scaling 4.7x parameters with minimal impact on RTF0
U2++: Unified Two-pass Bidirectional End-to-end Model for Speech Recognition0
Uconv-Conformer: High Reduction of Input Sequence Length for End-to-End Speech Recognition0
UCorrect: An Unsupervised Framework for Automatic Speech Recognition Error Correction0
UFO2: A unified pre-training framework for online and offline speech recognition0
Ultra-low Power Always-on Intelligent and Connected SNN-based System for Multimedia IoT-enabled Applications0
UME: Upcycling Mixture-of-Experts for Scalable and Efficient Automatic Speech Recognition0
UML: A Universal Monolingual Output Layer for Multilingual ASR0
Uncertain Out-of-Domain Generalization0
Uncertainty-guided Model Generalization to Unseen Domains0
Uncertainty Estimation in Autoregressive Structured Prediction0
Uncovering the Functional Roles of Nonlinearity in Memory0
Uncovering the Visual Contribution in Audio-Visual Speech Recognition0
Underspecification in Natural Language Understanding for Dialog Automation0
Gradient-Leaks: Understanding and Controlling Deanonymization in Federated Learning0
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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