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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 651–700 of 6433 papers

TitleStatusHype
Qwen vs. Gemma Integration with Whisper: A Comparative Study in Multilingual SpeechLLM Systems—0
BUT System for the MLC-SLM Challenge—0
Bi-directional Context-Enhanced Speech Large Language Models for Multilingual Conversational ASR—0
A Silent Speech Decoding System from EEG and EMG with Heterogenous Electrode Configurations—0
NTU Speechlab LLM-Based Multilingual ASR System for Interspeech MLC-SLM Challenge 2025—0
SC-SOT: Conditioning the Decoder on Diarized Speaker Information for End-to-End Overlapped Speech Recognition—0
Lightweight and Robust Multi-Channel End-to-End Speech Recognition with Spherical Harmonic Transform—0
(SimPhon Speech Test): A Data-Driven Method for In Silico Design and Validation of a Phonetically Balanced Speech Test—0
Enabling automatic transcription of child-centered audio recordings from real-world environments—0
FairASR: Fair Audio Contrastive Learning for Automatic Speech Recognition—0
Joint ASR and Speaker Role Tagging with Serialized Output Training—0
Improving Named Entity Transcription with Contextual LLM-based Revision—0
Advances in Small-Footprint Keyword Spotting: A Comprehensive Review of Efficient Models and AlgorithmsCode0
Regularizing Learnable Feature Extraction for Automatic Speech Recognition—0
OWSM-Biasing: Contextualizing Open Whisper-Style Speech Models for Automatic Speech Recognition with Dynamic Vocabulary—0
Addressing Pitfalls in Auditing Practices of Automatic Speech Recognition Technologies: A Case Study of People with AphasiaCode0
SimClass: A Classroom Speech Dataset Generated via Game Engine Simulation For Automatic Speech Recognition Research—0
Transcript-Prompted Whisper with Dictionary-Enhanced Decoding for Japanese Speech Annotation—0
Benchmarking Foundation Speech and Language Models for Alzheimer's Disease and Related Dementia Detection from Spontaneous Speech—0
Uncovering the Functional Roles of Nonlinearity in Memory—0
Unified Semi-Supervised Pipeline for Automatic Speech Recognition—0
Speaker-Distinguishable CTC: Learning Speaker Distinction Using CTC for Multi-Talker Speech Recognition—0
Speech Recognition on TV Series with Video-guided Post-Correction—0
Automatic Speech Recognition of African American English: Lexical and Contextual Effects—0
Beyond Classification: Towards Speech Emotion Reasoning with Multitask AudioLLMs—0
Lightweight Prompt Biasing for Contextualized End-to-End ASR Systems—0
Diarization-Aware Multi-Speaker Automatic Speech Recognition via Large Language Models—0
Low-Resource Domain Adaptation for Speech LLMs via Text-Only Fine-Tuning—0
AS-ASR: A Lightweight Framework for Aphasia-Specific Automatic Speech Recognition—0
Bridging the Modality Gap: Softly Discretizing Audio Representation for LLM-based Automatic Speech Recognition—0
LESS: Large Language Model Enhanced Semi-Supervised Learning for Speech Foundational Models—0
Customizing Speech Recognition Model with Large Language Model Feedback—0
ViCocktail: Automated Multi-Modal Data Collection for Vietnamese Audio-Visual Speech Recognition—0
Better Pseudo-labeling with Multi-ASR Fusion and Error Correction by SpeechLLM—0
LLM-based phoneme-to-grapheme for phoneme-based speech recognition—0
Structured State Space Model Dynamics and Parametrization for Spiking Neural NetworksCode0
Effects of Speaker Count, Duration, and Accent Diversity on Zero-Shot Accent Robustness in Low-Resource ASR—0
MFLA: Monotonic Finite Look-ahead Attention for Streaming Speech Recognition—0
Improving Child Speech Recognition and Reading Mistake Detection by Using Prompts—0
A Multi-Dialectal Dataset for German Dialect ASR and Dialect-to-Standard Speech Translation—0
Enhancing Lyrics Transcription on Music Mixtures with Consistency Loss—0
Overcoming Data Scarcity in Multi-Dialectal Arabic ASR via Whisper Fine-Tuning—0
TalTech Systems for the Interspeech 2025 ML-SUPERB 2.0 Challenge—0
Unsupervised Rhythm and Voice Conversion to Improve ASR on Dysarthric SpeechCode0
Analyzing the Importance of Blank for CTC-Based Knowledge Distillation—0
Whale: Large-Scale multilingual ASR model with w2v-BERT and E-Branchformer with large speech data—0
Riemannian Time Warping: Multiple Sequence Alignment in Curved Spaces—0
WCTC-Biasing: Retraining-free Contextual Biasing ASR with Wildcard CTC-based Keyword Spotting and Inter-layer Biasing—0
Self-Supervised Speech Quality Assessment (S3QA): Leveraging Speech Foundation Models for a Scalable Speech Quality Metric—0
HENT-SRT: Hierarchical Efficient Neural Transducer with Self-Distillation for Joint Speech Recognition and Translation—0
Show:102550
← PrevPage 14 of 129Next →
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