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 1–50 of 6433 papers

TitleStatusHype
NonverbalTTS: A Public English Corpus of Text-Aligned Nonverbal Vocalizations with Emotion Annotations for Text-to-Speech—0
Task-Specific Audio Coding for Machines: Machine-Learned Latent Features Are Codes for That Machine—0
WhisperKit: On-device Real-time ASR with Billion-Scale Transformers—0
VisualSpeaker: Visually-Guided 3D Avatar Lip Synthesis—0
A Hybrid Machine Learning Framework for Optimizing Crop Selection via Agronomic and Economic Forecasting—0
First Steps Towards Voice Anonymization for Code-Switching SpeechCode0
MambAttention: Mamba with Multi-Head Attention for Generalizable Single-Channel Speech EnhancementCode2
VOICE CONTROL ROBOT USING ARDUINO MANAGEMENT SYSTEM PROJECT.—0
Lightweight Target-Speaker-Based Overlap Transcription for Practical Streaming ASR—0
Multimodal Representation Learning and Fusion—0
AUTOMATIC PRONUNCIATION MISTAKE DETECTOR PROJECT REPORT—0
End-to-End Spoken Grammatical Error Correction—0
AI-Generated Song Detection via Lyrics TranscriptsCode0
Splitformer: An improved early-exit architecture for automatic speech recognition on edge devicesCode0
OpusLM: A Family of Open Unified Speech Language Models—0
State-Space Models in Efficient Whispered and Multi-dialect Speech Recognition—0
LM-SPT: LM-Aligned Semantic Distillation for Speech Tokenization—0
Breaking the Transcription Bottleneck: Fine-tuning ASR Models for Extremely Low-Resource Fieldwork Languages—0
Weight Factorization and Centralization for Continual Learning in Speech Recognition—0
Automatic Speech Recognition Biases in Newcastle English: an Error Analysis—0
Thinking in Directivity: Speech Large Language Model for Multi-Talker Directional Speech Recognition—0
Unifying Streaming and Non-streaming Zipformer-based ASR—0
Improving Practical Aspects of End-to-End Multi-Talker Speech Recognition for Online and Offline Scenarios—0
NTU Speechlab LLM-Based Multilingual ASR System for Interspeech MLC-SLM Challenge 2025—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
Qwen vs. Gemma Integration with Whisper: A Comparative Study in Multilingual SpeechLLM Systems—0
Seewo's Submission to MLC-SLM: Lessons learned from Speech Reasoning Language Models—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
Advances in Small-Footprint Keyword Spotting: A Comprehensive Review of Efficient Models and AlgorithmsCode0
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
OWSM-Biasing: Contextualizing Open Whisper-Style Speech Models for Automatic Speech Recognition with Dynamic Vocabulary—0
Regularizing Learnable Feature Extraction for Automatic Speech Recognition—0
SimClass: A Classroom Speech Dataset Generated via Game Engine Simulation For Automatic Speech Recognition Research—0
Addressing Pitfalls in Auditing Practices of Automatic Speech Recognition Technologies: A Case Study of People with AphasiaCode0
Benchmarking Foundation Speech and Language Models for Alzheimer's Disease and Related Dementia Detection from Spontaneous Speech—0
Speaker-Distinguishable CTC: Learning Speaker Distinction Using CTC for Multi-Talker Speech Recognition—0
Transcript-Prompted Whisper with Dictionary-Enhanced Decoding for Japanese Speech Annotation—0
Unified Semi-Supervised Pipeline for Automatic Speech Recognition—0
Uncovering the Functional Roles of Nonlinearity in Memory—0
Speech Recognition on TV Series with Video-guided Post-Correction—0
Beyond Classification: Towards Speech Emotion Reasoning with Multitask AudioLLMs—0
Automatic Speech Recognition of African American English: Lexical and Contextual Effects—0
AS-ASR: A Lightweight Framework for Aphasia-Specific Automatic Speech Recognition—0
Show:102550
← PrevPage 1 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
9Gated ConvNetsWord Error Rate (WER)4.8—Unverified
10HMM-TDNN + iVectorsWord 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
7HMM-TDNN + pNorm + speed up/down speechPercentage error12.9—Unverified
8DNN MPEPercentage error12.9—Unverified
9DNN MMIPercentage error12.9—Unverified
10CNN + Bi-RNN + CTC (speech to letters), 25.9% WER if trainedonlyon SWBPercentage 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
6TC-DNN-BLSTM-DNNWord Error Rate (WER)3.5—Unverified
7Convolutional Speech RecognitionWord 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