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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 101–150 of 6433 papers

TitleStatusHype
Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis—0
Topological Deep Learning for Speech Data—0
Loquacious Set: 25,000 Hours of Transcribed and Diverse English Speech Recognition Data for Research and Commercial Use—0
PSRB: A Comprehensive Benchmark for Evaluating Persian ASR Systems—0
In-context Language Learning for Endangered Languages in Speech Recognition—0
The NaijaVoices Dataset: Cultivating Large-Scale, High-Quality, Culturally-Rich Speech Data for African Languages—0
Robust fine-tuning of speech recognition models via model merging: application to disordered speech—0
Beyond Manual Transcripts: The Potential of Automated Speech Recognition Errors in Improving Alzheimer's Disease Detection—0
Continuous Learning for Children's ASR: Overcoming Catastrophic Forgetting with Elastic Weight Consolidation and Synaptic Intelligence—0
Mixture of LoRA Experts for Low-Resourced Multi-Accent Automatic Speech Recognition—0
KIT's Low-resource Speech Translation Systems for IWSLT2025: System Enhancement with Synthetic Data and Model Regularization—0
Languages in Multilingual Speech Foundation Models Align Both Phonetically and Semantically—0
Exploring Generative Error Correction for Dysarthric Speech RecognitionCode0
Novel Loss-Enhanced Universal Adversarial Patches for Sustainable Speaker Privacy—0
WhisperD: Dementia Speech Recognition and Filler Word Detection with Whisper—0
CHSER: A Dataset and Case Study on Generative Speech Error Correction for Child ASRCode0
Building a Functional Machine Translation Corpus for Kpelle—0
StandUp4AI: A New Multilingual Dataset for Humor Detection in Stand-up Comedy Videos—0
VietASR: Achieving Industry-level Vietnamese ASR with 50-hour labeled data and Large-Scale Speech Pretraining—0
Swedish Whispers; Leveraging a Massive Speech Corpus for Swedish Speech Recognition—0
LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic ContextCode0
CosyVoice 3: Towards In-the-wild Speech Generation via Scaling-up and Post-trainingCode11
Speechless: Speech Instruction Training Without Speech for Low Resource LanguagesCode7
Daily-Omni: Towards Audio-Visual Reasoning with Temporal Alignment across ModalitiesCode1
SoccerChat: Integrating Multimodal Data for Enhanced Soccer Game UnderstandingCode0
An Effective Training Framework for Light-Weight Automatic Speech Recognition Models—0
Large Language Models based ASR Error Correction for Child Conversations—0
From Tens of Hours to Tens of Thousands: Scaling Back-Translation for Speech RecognitionCode1
Word Level Timestamp Generation for Automatic Speech Recognition and TranslationCode0
From Weak Labels to Strong Results: Utilizing 5,000 Hours of Noisy Classroom Transcripts with Minimal Accurate Data—0
Impact of Frame Rates on Speech Tokenizer: A Case Study on Mandarin and English—0
In-Context Learning Boosts Speech Recognition via Human-like Adaptation to Speakers and Language Varieties—0
Towards Inclusive ASR: Investigating Voice Conversion for Dysarthric Speech Recognition in Low-Resource LanguagesCode0
Dual Precision Quantization for Efficient and Accurate Deep Neural Networks InferenceCode0
Transfer Learning from Visual Speech Recognition to Mouthing Recognition in German Sign LanguageCode0
HausaNLP: Current Status, Challenges and Future Directions for Hausa Natural Language Processing—0
The Multimodal Information Based Speech Processing (MISP) 2025 Challenge: Audio-Visual Diarization and Recognition—0
Scaling and Enhancing LLM-based AVSR: A Sparse Mixture of Projectors Approach—0
PersonaTAB: Predicting Personality Traits using Textual, Acoustic, and Behavioral Cues in Fully-Duplex Speech DialogsCode0
Multi-head Temporal Latent AttentionCode4
Granary: Speech Recognition and Translation Dataset in 25 European Languages—0
Calm-Whisper: Reduce Whisper Hallucination On Non-Speech By Calming Crazy Heads Down—0
KIT's Offline Speech Translation and Instruction Following Submission for IWSLT 2025—0
Cross-modal Knowledge Transfer Learning as Graph Matching Based on Optimal Transport for ASR—0
ASR-FAIRBENCH: Measuring and Benchmarking Equity Across Speech Recognition Systems—0
Automatic Speech Recognition for African Low-Resource Languages: Challenges and Future Directions—0
LipDiffuser: Lip-to-Speech Generation with Conditional Diffusion Models—0
LegoSLM: Connecting LLM with Speech Encoder using CTC Posteriors—0
Survey of End-to-End Multi-Speaker Automatic Speech Recognition for Monaural Audio—0
Multi-Stage Speaker Diarization for Noisy ClassroomsCode0
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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