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 851–900 of 6433 papers

TitleStatusHype
WavRAG: Audio-Integrated Retrieval Augmented Generation for Spoken Dialogue Models—0
Measuring the Effect of Transcription Noise on Downstream Language Understanding TasksCode0
Adopting Whisper for Confidence Estimation—0
Gesture-Aware Zero-Shot Speech Recognition for Patients with Language Disorders—0
Neuro-oscillatory models of cortical speech processing—0
On the Robust Approximation of ASR Metrics—0
Lost in Transcription, Found in Distribution Shift: Demystifying Hallucination in Speech Foundation Models—0
Speech-FT: Merging Pre-trained And Fine-Tuned Speech Representation Models For Cross-Task Generalization—0
Benchmarking Automatic Speech Recognition coupled LLM Modules for Medical Diagnostics—0
NaturalL2S: End-to-End High-quality Multispeaker Lip-to-Speech Synthesis with Differential Digital Signal Processing—0
OWLS: Scaling Laws for Multilingual Speech Recognition and Translation Models—0
Microphone Array Geometry Independent Multi-Talker Distant ASR: NTT System for the DASR Task of the CHiME-8 Challenge—0
A Preliminary Exploration with GPT-4o Voice Mode—0
MTLM: Incorporating Bidirectional Text Information to Enhance Language Model Training in Speech Recognition Systems—0
Shortcut Learning Susceptibility in Vision Classifiers—0
Causal Analysis of ASR Errors for Children: Quantifying the Impact of Physiological, Cognitive, and Extrinsic Factors—0
MoHAVE: Mixture of Hierarchical Audio-Visual Experts for Robust Speech Recognition—0
Speech to Speech Translation with Translatotron: A State of the Art Review—0
Lightweight Operations for Visual Speech Recognition—0
Evaluating Standard and Dialectal Frisian ASR: Multilingual Fine-tuning and Language Identification for Improved Low-resource Performance—0
Koel-TTS: Enhancing LLM based Speech Generation with Preference Alignment and Classifier Free Guidance—0
Aligner-Encoders: Self-Attention Transformers Can Be Self-Transducers—0
Afrispeech-Dialog: A Benchmark Dataset for Spontaneous English Conversations in Healthcare and Beyond—0
Leveraging Broadcast Media Subtitle Transcripts for Automatic Speech Recognition and SubtitlingCode0
Adapter-Based Multi-Agent AVSR Extension for Pre-Trained ASR Models—0
Gradient Norm-based Fine-Tuning for Backdoor Defense in Automatic Speech Recognition—0
CTC-DRO: Robust Optimization for Reducing Language Disparities in Speech Recognition—0
A Differentiable Alignment Framework for Sequence-to-Sequence Modeling via Optimal Transport—0
Evaluation of End-to-End Continuous Spanish Lipreading in Different Data ConditionsCode0
Data-Driven Mispronunciation Pattern Discovery for Robust Speech Recognition—0
When End-to-End is Overkill: Rethinking Cascaded Speech-to-Text Translation—0
SELMA: A Speech-Enabled Language Model for Virtual Assistant Interactions—0
Language Bias in Self-Supervised Learning For Automatic Speech Recognition—0
DyPCL: Dynamic Phoneme-level Contrastive Learning for Dysarthric Speech Recognition—0
Privacy-Preserving Edge Speech Understanding with Tiny Foundation Models—0
Cross-lingual Embedding Clustering for Hierarchical Softmax in Low-Resource Multilingual Speech Recognition—0
SCDiar: a streaming diarization system based on speaker change detection and speech recognition—0
RDMM: Fine-Tuned LLM Models for On-Device Robotic Decision Making with Enhanced Contextual Awareness in Specific DomainsCode0
Classification Error Bound for Low Bayes Error Conditions in Machine Learning—0
End-to-End Target Speaker Speech Recognition Using Context-Aware Attention Mechanisms for Challenging Enrollment Scenario—0
SEAL: Speech Embedding Alignment Learning for Speech Large Language Model with Retrieval-Augmented Generation—0
Robust Cross-Etiology and Speaker-Independent Dysarthric Speech Recognition—0
The Multicultural Medical Assistant: Can LLMs Improve Medical ASR Errors Across Borders?—0
Speech Translation Refinement using Large Language ModelsCode0
LoCoML: A Framework for Real-World ML Inference Pipelines—0
Learning-based A Posteriori Speech Presence Probability Estimation and Applications—0
Integrating Persian Lip Reading in Surena-V Humanoid Robot for Human-Robot Interaction—0
DQ-Data2vec: Decoupling Quantization for Multilingual Speech Recognition—0
Predicting Compact Phrasal Rewrites with Large Language Models for ASR Post Editing—0
Development of an Inclusive Educational Platform Using Open Technologies and Machine Learning: A Case Study on Accessibility Enhancement—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
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