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 801–850 of 6433 papers

TitleStatusHype
Transformer-Based Named Entity Recognition for Automated Server ProvisioningCode0
Whispering Under the Eaves: Protecting User Privacy Against Commercial and LLM-powered Automatic Speech Recognition SystemsCode0
Scaling Auditory Cognition via Test-Time Compute in Audio Language Models—0
The Impact of Code-switched Synthetic Data Quality is Task Dependent: Insights from MT and ASR—0
VALLR: Visual ASR Language Model for Lip Reading—0
A 71.2-μW Speech Recognition Accelerator with Recurrent Spiking Neural Network—0
Efficient First-Order Optimization on the Pareto Set for Multi-Objective Learning under Preference Guidance—0
FinAudio: A Benchmark for Audio Large Language Models in Financial Applications—0
Improving Speech Recognition Accuracy Using Custom Language Models with the Vosk Toolkit—0
Boosting the Transferability of Audio Adversarial Examples with Acoustic Representation Optimization—0
Contextual Metric Meta-Evaluation by Measuring Local Metric Accuracy—0
Coverage-Guaranteed Speech Emotion Recognition via Calibrated Uncertainty-Adaptive Prediction Sets—0
Whispering in Amharic: Fine-tuning Whisper for Low-resource Language—0
From S4 to Mamba: A Comprehensive Survey on Structured State Space Models—0
Your voice is your voice: Supporting Self-expression through Speech Generation and LLMs in Augmented and Alternative Communication—0
SeniorTalk: A Chinese Conversation Dataset with Rich Annotations for Super-Aged Seniors—0
A Comprehensive Survey on Architectural Advances in Deep CNNs: Challenges, Applications, and Emerging Research Directions—0
Evaluating ASR Confidence Scores for Automated Error Detection in User-Assisted Correction Interfaces—0
Halving transcription time: A fast, user-friendly and GDPR-compliant workflow to create AI-assisted transcripts for content analysis—0
Enhancing Aviation Communication Transcription: Fine-Tuning Distil-Whisper with LoRA—0
ValSub: Subsampling Validation Data to Mitigate Forgetting during ASR Personalization—0
Proceedings of the ISCA/ITG Workshop on Diversity in Large Speech and Language Models—0
Quantization for OpenAI's Whisper Models: A Comparative AnalysisCode0
Everything Can Be Described in Words: A Simple Unified Multi-Modal Framework with Semantic and Temporal Alignment—0
Lend a Hand: Semi Training-Free Cued Speech Recognition via MLLM-Driven Hand Modeling for Barrier-free CommunicationCode0
An Exhaustive Evaluation of TTS- and VC-based Data Augmentation for ASR—0
Automatic Speech Recognition for Non-Native English: Accuracy and Disfluency Handling—0
Building English ASR model with regional language support—0
Adaptive Audio-Visual Speech Recognition via Matryoshka-Based Multimodal LLMs—0
A Causal Inference Approach for Quantifying Research Impact—0
Self-Supervised Models for Phoneme Recognition: Applications in Children's Speech for Reading Learning—0
From Voice to Safety: Language AI Powered Pilot-ATC Communication Understanding for Airport Surface Movement Collision Risk Assessment—0
Qieemo: Speech Is All You Need in the Emotion Recognition in Conversations—0
CORDIC Is All You Need—0
Fine-Tuning Whisper for Inclusive Prosodic Stress Analysis—0
Direct Speech to Speech Translation: A Review—0
UniWav: Towards Unified Pre-training for Speech Representation Learning and Generation—0
Unveiling Biases while Embracing Sustainability: Assessing the Dual Challenges of Automatic Speech Recognition Systems—0
Adapting Automatic Speech Recognition for Accented Air Traffic Control Communications—0
CS-Dialogue: A 104-Hour Dataset of Spontaneous Mandarin-English Code-Switching Dialogues for Speech Recognition—0
Nexus: An Omni-Perceptive And -Interactive Model for Language, Audio, And Vision—0
Exploring Gender Disparities in Automatic Speech Recognition Technology—0
Improving the Inclusivity of Dutch Speech Recognition by Fine-tuning Whisper on the JASMIN-CGN CorpusCode0
Balancing Speech Understanding and Generation Using Continual Pre-training for Codec-based Speech LLM—0
Low-Rank and Sparse Model Merging for Multi-Lingual Speech Recognition and Translation—0
Understanding Zero-shot Rare Word Recognition Improvements Through LLM Integration—0
The Esethu Framework: Reimagining Sustainable Dataset Governance and Curation for Low-Resource Languages—0
Retrieval-Augmented Speech Recognition Approach for Domain Challenges—0
Enhancing Speech Large Language Models with Prompt-Aware Mixture of Audio Encoders—0
WavRAG: Audio-Integrated Retrieval Augmented Generation for Spoken Dialogue Models—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