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 25012550 of 6433 papers

TitleStatusHype
Closing the Gap Between Time-Domain Multi-Channel Speech Enhancement on Real and Simulation Conditions0
Advocating Character Error Rate for Multilingual ASR Evaluation0
Fine-Tuning Whisper for Inclusive Prosodic Stress Analysis0
Closing the Gap between Single-User and Multi-User VoiceFilter-Lite0
Clipping Free Attacks Against Neural Networks0
Clipping free attacks against artificial neural networks0
ArEEG_Words: Dataset for Envisioned Speech Recognition using EEG for Arabic Words0
Adversary Resistant Deep Neural Networks with an Application to Malware Detection0
Acoustic and Textual Data Augmentation for Improved ASR of Code-Switching Speech0
Clinical Dialogue Transcription Error Correction using Seq2Seq Models0
Clinical BERTScore: An Improved Measure of Automatic Speech Recognition Performance in Clinical Settings0
ArEEG_Chars: Dataset for Envisioned Speech Recognition using EEG for Arabic Characters0
Click or Type: An Analysis of Wizard's Interaction for Future Wizard Interface Design0
Findings of the 2024 Mandarin Stuttering Event Detection and Automatic Speech Recognition Challenge0
Are E2E ASR models ready for an industrial usage?0
Adversarial Training of End-to-end Speech Recognition Using a Criticizing Language Model0
Clean Label Attacks against SLU Systems0
Cleanformer: A multichannel array configuration-invariant neural enhancement frontend for ASR in smart speakers0
Are disentangled representations all you need to build speaker anonymization systems?0
Fillers in Spoken Language Understanding: Computational and Psycholinguistic Perspectives0
Class LM and word mapping for contextual biasing in End-to-End ASR0
FFT-Based Deep Learning Deployment in Embedded Systems0
FfDL : A Flexible Multi-tenant Deep Learning Platform0
Classist Tools: Social Class Correlates with Performance in NLP0
A Recorded Debating Dataset0
Adversarial Training for Multilingual Acoustic Modeling0
Acoustically Grounded Word Embeddings for Improved Acoustics-to-Word Speech Recognition0
Few-shot learning with attention-based sequence-to-sequence models0
Fewer Hallucinations, More Verification: A Three-Stage LLM-Based Framework for ASR Error Correction0
Filter and evolve: progressive pseudo label refining for semi-supervised automatic speech recognition0
Filter-based Discriminative Autoencoders for Children Speech Recognition0
FinAudio: A Benchmark for Audio Large Language Models in Financial Applications0
Findings from Experiments of On-line Joint Reinforcement Learning of Semantic Parser and Dialogue Manager with real Users0
Findings of the 2023 ML-SUPERB Challenge: Pre-Training and Evaluation over More Languages and Beyond0
Classifying Dialogue Acts in Multi-party Live Chats0
Findings of the Shared Task on Speech Recognition for Vulnerable Individuals in Tamil0
Finding Task-specific Subnetworks in Multi-task Spoken Language Understanding Model0
Fine-grained Generalization Analysis of Structured Output Prediction0
Feedforward Sequential Memory Networks: A New Structure to Learn Long-term Dependency0
Fine-Tuning Automatic Speech Recognition for People with Parkinson's: An Effective Strategy for Enhancing Speech Technology Accessibility0
Fine-tuning convergence model in Bengali speech recognition0
Finetuning End-to-End Models for Estonian Conversational Spoken Language Translation0
Fine-tuning of Pre-trained End-to-end Speech Recognition with Generative Adversarial Networks0
Fine-tuning pre-trained models for Automatic Speech Recognition, experiments on a fieldwork corpus of Japhug (Trans-Himalayan family)0
Feed Forward and Backward Run in Deep Convolution Neural Network0
Classifying Arab Names Geographically0
A Real-time Robot-based Auxiliary System for Risk Evaluation of COVID-19 Infection0
Finite-State Acoustic and Translation Model Composition in Statistical Speech Translation: Empirical Assessment0
FedNST: Federated Noisy Student Training for Automatic Speech Recognition0
Classification of Closely Related Sub-dialects of Arabic Using Support-Vector Machines0
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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