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 901–950 of 6433 papers

TitleStatusHype
Let SSMs be ConvNets: State-space Modeling with Optimal Tensor ContractionsCode0
A Domain Adaptation Framework for Speech Recognition Systems with Only Synthetic data—0
Investigation of Whisper ASR Hallucinations Induced by Non-Speech Audio—0
Generative AI and Large Language Models in Language Preservation: Opportunities and Challenges—0
Enhancing Neural Spoken Language Recognition: An Exploration with Multilingual Datasets—0
A Benchmark of French ASR Systems Based on Error Severity—0
GEC-RAG: Improving Generative Error Correction via Retrieval-Augmented Generation for Automatic Speech Recognition Systems—0
Automatic Speech Recognition for Sanskrit with Transfer Learning—0
Unsupervised Rhythm and Voice Conversion of Dysarthric to Healthy Speech for ASR—0
Delayed Fusion: Integrating Large Language Models into First-Pass Decoding in End-to-end Speech Recognition—0
PIER: A Novel Metric for Evaluating What Matters in Code-SwitchingCode0
Teaching Wav2Vec2 the Language of the BrainCode0
Adapting Whisper for Regional Dialects: Enhancing Public Services for Vulnerable Populations in the United Kingdom—0
persoDA: Personalized Data Augmentation for Personalized ASR—0
A Non-autoregressive Model for Joint STT and TTS—0
Selective Attention Merging for low resource tasks: A case study of Child ASRCode0
Loudspeaker Beamforming to Enhance Speech Recognition Performance of Voice Driven Applications—0
Audio-CoT: Exploring Chain-of-Thought Reasoning in Large Audio Language Model—0
AdaCS: Adaptive Normalization for Enhanced Code-Switching ASRCode0
Joint Automatic Speech Recognition And Structure Learning For Better Speech UnderstandingCode0
Speech Recognition for Automatically Assessing Afrikaans and isiXhosa Preschool Oral Narratives—0
A Survey on Spoken Italian Datasets and Corpora—0
Discrete Speech Unit Extraction via Independent Component AnalysisCode0
Comparing Self-Supervised Learning Models Pre-Trained on Human Speech and Animal Vocalizations for Bioacoustics ProcessingCode0
Benchmarking Rotary Position Embeddings for Automatic Speech Recognition—0
Universal-2-TF: Robust All-Neural Text Formatting for ASR—0
Contextual ASR Error Handling with LLMs Augmentation for Goal-Oriented Conversational AI—0
TTS-Transducer: End-to-End Speech Synthesis with Neural Transducer—0
Fleurs-SLU: A Massively Multilingual Benchmark for Spoken Language UnderstandingCode0
LipGen: Viseme-Guided Lip Video Generation for Enhancing Visual Speech Recognition—0
Methods to Increase the Amount of Data for Speech Recognition for Low Resource Languages—0
Universal Speaker Embedding Free Target Speaker Extraction and Personal Voice Activity Detection—0
Deep Learning for Pathological Speech: A Survey—0
Towards a Generalizable Speech Marker for Parkinson's Disease Diagnosis—0
Samba-ASR: State-Of-The-Art Speech Recognition Leveraging Structured State-Space Models—0
Listening and Seeing Again: Generative Error Correction for Audio-Visual Speech RecognitionCode0
Improving Transducer-Based Spoken Language Understanding with Self-Conditioned CTC and Knowledge Transfer—0
Advancing Singlish Understanding: Bridging the Gap with Datasets and Multimodal ModelsCode0
Automatic Text Pronunciation Correlation Generation and Application for Contextual Biasing—0
Breaking Through the Spike: Spike Window Decoding for Accelerated and Precise Automatic Speech Recognition—0
Incremental Dialogue Management: Survey, Discussion, and Implications for HRI—0
LiveCC: Learning Video LLM with Streaming Speech Transcription at Scale—0
Whisper Turns Stronger: Augmenting Wav2Vec 2.0 for Superior ASR in Low-Resource Languages—0
Fotheidil: an Automatic Transcription System for the Irish Language—0
Enhancing Whisper's Accuracy and Speed for Indian Languages through Prompt-Tuning and Tokenization—0
Towards a Single ASR Model That Generalizes to Disordered Speech—0
Enhancing Audiovisual Speech Recognition through Bifocal Preference Optimization—0
Structured Speaker-Deficiency Adaptation of Foundation Models for Dysarthric and Elderly Speech Recognition—0
Speech Recognition With LLMs Adapted to Disordered Speech Using Reinforcement Learning—0
Zero-resource Speech Translation and Recognition with LLMs—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