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 951–1000 of 6433 papers

TitleStatusHype
Trading Devil RL: Backdoor attack via Stock market, Bayesian Optimization and Reinforcement Learning—0
Deep Learning in Proteomics Informatics: Applications, Challenges, and Future Directions—0
Investigating Prosodic Signatures via Speech Pre-Trained Models for Audio Deepfake Source Attribution—0
UME: Upcycling Mixture-of-Experts for Scalable and Efficient Automatic Speech Recognition—0
Uncovering the Visual Contribution in Audio-Visual Speech Recognition—0
Adapting Whisper for Code-Switching through Encoding Refining and Language-Aware Decoding—0
Transducer-Llama: Integrating LLMs into Streamable Transducer-based Speech Recognition—0
Enhancing Multilingual ASR for Unseen Languages via Language Embedding Modeling—0
Speech Retrieval-Augmented Generation without Automatic Speech Recognition—0
TouchASP: Elastic Automatic Speech Perception that Everyone Can Touch—0
Transcribing and Translating, Fast and Slow: Joint Speech Translation and Recognition—0
LAMA-UT: Language Agnostic Multilingual ASR through Orthography Unification and Language-Specific Transliteration—0
MERaLiON-SpeechEncoder: Towards a Speech Foundation Model for Singapore and Beyond—0
Speak & Improve Challenge 2025: Tasks and Baseline Systems—0
Speak & Improve Corpus 2025: an L2 English Speech Corpus for Language Assessment and Feedback—0
Transliterated Zero-Shot Domain Adaptation for Automatic Speech Recognition—0
Robust Persian Digit Recognition in Noisy Environments Using Hybrid CNN-BiGRU Model—0
Efficient Adaptation of Multilingual Models for Japanese ASRCode0
MERaLiON-AudioLLM: Bridging Audio and Language with Large Language Models—0
Greek2MathTex: A Greek Speech-to-Text Framework for LaTeX Equations GenerationCode0
Bilevel Joint Unsupervised and Supervised Training for Automatic Speech Recognition—0
Style-agnostic evaluation of ASR using multiple reference transcripts—0
Effective Text Adaptation for LLM-based ASR through Soft Prompt Fine-Tuning—0
Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation—0
Not All Errors Are Equal: Investigation of Speech Recognition Errors in Alzheimer's Disease Detection—0
Leveraging Prompt Learning and Pause Encoding for Alzheimer's Disease Detection—0
Harnessing Transfer Learning from Swahili: Advancing Solutions for Comorian Dialects—0
SQ-Whisper: Speaker-Querying based Whisper Model for Target-Speaker ASRCode0
Adaptive Dropout for Pruning Conformers—0
Comprehensive Audio Query Handling System with Integrated Expert Models and Contextual Understanding—0
Speech Recognition-based Feature Extraction for Enhanced Automatic Severity Classification in Dysarthric Speech—0
ASR-EC Benchmark: Evaluating Large Language Models on Chinese ASR Error Correction—0
Automating Feedback Analysis in Surgical Training: Detection, Categorization, and AssessmentCode0
Late fusion ensembles for speech recognition on diverse input audio representations—0
A Comparative Study of LLM-based ASR and Whisper in Low Resource and Code Switching Scenario—0
Empowering the Deaf and Hard of Hearing Community: Enhancing Video Captions Using Large Language Models—0
ArEEG_Words: Dataset for Envisioned Speech Recognition using EEG for Arabic Words—0
How to Learn a New Language? An Efficient Solution for Self-Supervised Learning Models Unseen Languages Adaption in Low-Resource Scenario—0
MSA-ASR: Efficient Multilingual Speaker Attribution with frozen ASR Models—0
AMPS: ASR with Multimodal Paraphrase Supervision—0
Aligning Pre-trained Models for Spoken Language Translation—0
Continual Learning in Machine Speech Chain Using Gradient Episodic Memory—0
SALMONN-omni: A Codec-free LLM for Full-duplex Speech Understanding and Generation—0
Enhancing Code-Switching ASR Leveraging Non-Peaky CTC Loss and Deep Language Posterior Injection—0
k2SSL: A Faster and Better Framework for Self-Supervised Speech Representation Learning—0
Towards Maximum Likelihood Training for Transducer-based Streaming Speech Recognition—0
Disentangled-Transformer: An Explainable End-to-End Automatic Speech Recognition Model with Speech Content-Context Separation—0
High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR—0
TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks—0
Transforming NLU with Babylon: A Case Study in Development of Real-time, Edge-Efficient, Multi-Intent Translation System for Automated Drive-Thru Ordering—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