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

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Showing 1151–1200 of 6433 papers

TitleStatusHype
Evaluation of real-time transcriptions using end-to-end ASR models—0
Consensus-based Distributed Quantum Kernel Learning for Speech Recognition—0
Findings of the 2024 Mandarin Stuttering Event Detection and Automatic Speech Recognition Challenge—0
A Toolkit for Joint Speaker Diarization and Identification with Application to Speaker-Attributed ASR—0
NTT Multi-Speaker ASR System for the DASR Task of CHiME-8 Challenge—0
Retrieval Augmented Correction of Named Entity Speech Recognition Errors—0
Longer is (Not Necessarily) Stronger: Punctuated Long-Sequence Training for Enhanced Speech Recognition and Translation—0
An investigation of modularity for noise robustness in conformer-based ASR—0
Exploring WavLM Back-ends for Speech Spoofing and Deepfake Detection—0
Lightweight Transducer Based on Frame-Level CriterionCode0
What is lost in Normalization? Exploring Pitfalls in Multilingual ASR Model Evaluations—0
Efficient Extraction of Noise-Robust Discrete Units from Self-Supervised Speech Models—0
Probing self-attention in self-supervised speech models for cross-linguistic differences—0
Quantification of stylistic differences in human- and ASR-produced transcripts of African American English—0
Enhancing Code-Switching Speech Recognition with LID-Based Collaborative Mixture of Experts Model—0
VoxHakka: A Dialectally Diverse Multi-speaker Text-to-Speech System for Taiwanese Hakka—0
The USTC-NERCSLIP Systems for the CHiME-8 NOTSOFAR-1 Challenge—0
Temporal Order Preserved Optimal Transport-based Cross-modal Knowledge Transfer Learning for ASR—0
Reassessing Noise Augmentation Methods in the Context of Adversarial Speech—0
A Framework for Synthetic Audio Conversations Generation using Large Language Models—0
Resource-Efficient Adaptation of Speech Foundation Models for Multi-Speaker ASR—0
Serialized Speech Information Guidance with Overlapped Encoding Separation for Multi-Speaker Automatic Speech Recognition—0
Comparing Discrete and Continuous Space LLMs for Speech Recognition—0
Progressive Residual Extraction based Pre-training for Speech Representation Learning—0
DCIM-AVSR : Efficient Audio-Visual Speech Recognition via Dual Conformer Interaction Module—0
ProGRes: Prompted Generative Rescoring on ASR n-BestCode0
Developing an End-to-End Framework for Predicting the Social Communication Severity Scores of Children with Autism Spectrum Disorder—0
Advancing Multi-talker ASR Performance with Large Language Models—0
Speaker Tagging Correction With Non-Autoregressive Language Models—0
Benchmarking Japanese Speech Recognition on ASR-LLM Setups with Multi-Pass Augmented Generative Error Correction—0
Measuring the Accuracy of Automatic Speech Recognition SolutionsCode0
Revisit Micro-batch Clipping: Adaptive Data Pruning via Gradient Manipulation—0
Beyond Levenshtein: Leveraging Multiple Algorithms for Robust Word Error Rate Computations And Granular Error ClassificationsCode0
Speech Recognition Transformers: Topological-lingualism Perspective—0
Literary and Colloquial Dialect Identification for Tamil using Acoustic Features—0
Self-supervised Speech Representations Still Struggle with African American Vernacular EnglishCode0
Automatic recognition and detection of aphasic natural speech—0
Research Advances and New Paradigms for Biology-inspired Spiking Neural Networks—0
MEDSAGE: Enhancing Robustness of Medical Dialogue Summarization to ASR Errors with LLM-generated Synthetic Dialogues—0
Literary and Colloquial Tamil Dialect Identification—0
Studying the Effect of Audio Filters in Pre-Trained Models for Environmental Sound Classification—0
Focused Discriminative Training For Streaming CTC-Trained Automatic Speech Recognition Models—0
Developing vocal system impaired patient-aimed voice quality assessment approach using ASR representation-included multiple features—0
Positional Description for Numerical Normalization—0
Towards measuring fairness in speech recognition: Fair-Speech dataset—0
Improving Speech Recognition Error Prediction for Modern and Off-the-shelf Speech Recognizers—0
The State of Commercial Automatic French Legal Speech Recognition Systems and their Impact on Court Reporters et al—0
XCB: an effective contextual biasing approach to bias cross-lingual phrases in speech recognition—0
Recording for Eyes, Not Echoing to Ears: Contextualized Spoken-to-Written Conversion of ASR Transcripts—0
Parameter-Efficient Transfer Learning under Federated Learning for Automatic Speech Recognition—0
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All datasetsLibriSpeech test-cleanLibriSpeech test-otherSwitchboard + Hub500TIMITAISHELL-1WSJ eval92Common Voice Germanswb_hub_500 WER fullSWBCHTUDACommon Voice FrenchCommon Voice SpanishMediaSpeech

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AmNetWord Error Rate (WER)8.6—Unverified
2HMM-(SAT)GMMWord Error Rate (WER)8—Unverified
3Local Prior Matching (Large Model)Word Error Rate (WER)7.19—Unverified
4SnipsWord Error Rate (WER)6.4—Unverified
5Li-GRUWord Error Rate (WER)6.2—Unverified
6HMM-DNN + pNorm*Word Error Rate (WER)5.5—Unverified
7CTC + policy learningWord Error Rate (WER)5.42—Unverified
8Deep Speech 2Word Error Rate (WER)5.33—Unverified
9HMM-TDNN + iVectorsWord Error Rate (WER)4.8—Unverified
10Gated ConvNetsWord Error Rate (WER)4.8—Unverified
#ModelMetricClaimedVerifiedStatus
1Local Prior Matching (Large Model)Word Error Rate (WER)20.84—Unverified
2SnipsWord Error Rate (WER)16.5—Unverified
3Local Prior Matching (Large Model, ConvLM LM)Word Error Rate (WER)15.28—Unverified
4Deep Speech 2Word Error Rate (WER)13.25—Unverified
5TDNN + pNorm + speed up/down speechWord Error Rate (WER)12.5—Unverified
6CTC-CRF 4gram-LMWord Error Rate (WER)10.65—Unverified
7Convolutional Speech RecognitionWord Error Rate (WER)10.47—Unverified
8MT4SSLWord Error Rate (WER)9.6—Unverified
9Jasper DR 10x5Word Error Rate (WER)8.79—Unverified
10EspressoWord Error Rate (WER)8.7—Unverified
#ModelMetricClaimedVerifiedStatus
1Deep SpeechPercentage error20—Unverified
2DNN-HMMPercentage error18.5—Unverified
3CD-DNNPercentage error16.1—Unverified
4DNNPercentage error16—Unverified
5DNN + DropoutPercentage error15—Unverified
6DNN BMMIPercentage error12.9—Unverified
7DNN MPEPercentage error12.9—Unverified
8DNN MMIPercentage error12.9—Unverified
9HMM-TDNN + pNorm + speed up/down speechPercentage error12.9—Unverified
10HMM-DNN +sMBRPercentage error12.6—Unverified
#ModelMetricClaimedVerifiedStatus
1LSNNPercentage error33.2—Unverified
2LAS multitask with indicators samplingPercentage error20.4—Unverified
3Soft Monotonic Attention (ours, offline)Percentage error20.1—Unverified
4QCNN-10L-256FMPercentage error19.64—Unverified
5Bi-LSTM + skip connections w/ CTCPercentage error17.7—Unverified
6Bi-RNN + AttentionPercentage error17.6—Unverified
7RNN-CRF on 24(x3) MFSCPercentage error17.3—Unverified
8CNN in time and frequency + dropout, 17.6% w/o dropoutPercentage error16.7—Unverified
9Light Gated Recurrent UnitsPercentage error16.7—Unverified
10GRUPercentage error16.6—Unverified
#ModelMetricClaimedVerifiedStatus
1AttWord Error Rate (WER)18.7—Unverified
2CTC/AttWord Error Rate (WER)6.7—Unverified
3BRA-EWord Error Rate (WER)6.63—Unverified
4CTC-CRF 4gram-LMWord Error Rate (WER)6.34—Unverified
5BATWord Error Rate (WER)4.97—Unverified
6ParaformerWord Error Rate (WER)4.95—Unverified
7U2Word Error Rate (WER)4.72—Unverified
8UMAWord Error Rate (WER)4.7—Unverified
9Lightweight TransducerWord Error Rate (WER)4.31—Unverified
10CIF-HKD With LMWord Error Rate (WER)4.1—Unverified
#ModelMetricClaimedVerifiedStatus
1Jasper 10x3Word Error Rate (WER)6.9—Unverified
2CNN over RAW speech (wav)Word Error Rate (WER)5.6—Unverified
3CTC-CRF 4gram-LMWord Error Rate (WER)3.79—Unverified
4Deep Speech 2Word Error Rate (WER)3.6—Unverified
5test-set on open vocabulary (i.e. harder), model = HMM-DNN + pNorm*Word Error Rate (WER)3.6—Unverified
6Convolutional Speech RecognitionWord Error Rate (WER)3.5—Unverified
7TC-DNN-BLSTM-DNNWord Error Rate (WER)3.5—Unverified
8EspressoWord Error Rate (WER)3.4—Unverified
9CTC-CRF VGG-BLSTMWord Error Rate (WER)3.2—Unverified
10Transformer with Relaxed AttentionWord Error Rate (WER)3.19—Unverified