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 151–200 of 6433 papers

TitleStatusHype
Disentangling Speakers in Multi-Talker Speech Recognition with Speaker-Aware CTCCode1
Approaching Deep Learning through the Spectral Dynamics of WeightsCode1
SER Evals: In-domain and Out-of-domain Benchmarking for Speech Emotion RecognitionCode1
LI-TTA: Language Informed Test-Time Adaptation for Automatic Speech RecognitionCode1
ALIF: Low-Cost Adversarial Audio Attacks on Black-Box Speech Platforms using Linguistic FeaturesCode1
Enhancing Dysarthric Speech Recognition for Unseen Speakers via Prototype-Based AdaptationCode1
Dynamic Language Group-Based MoE: Enhancing Code-Switching Speech Recognition with Hierarchical RoutingCode1
Evolutionary Prompt Design for LLM-Based Post-ASR Error CorrectionCode1
dMel: Speech Tokenization made SimpleCode1
Framework for Curating Speech Datasets and Evaluating ASR Systems: A Case Study for PolishCode1
Vibravox: A Dataset of French Speech Captured with Body-conduction Audio SensorsCode1
Empowering Whisper as a Joint Multi-Talker and Target-Talker Speech Recognition SystemCode1
Tailored Design of Audio-Visual Speech Recognition Models using BranchformersCode1
Controlling Whisper: Universal Acoustic Adversarial Attacks to Control Speech Foundation ModelsCode1
Learning Video Temporal Dynamics with Cross-Modal Attention for Robust Audio-Visual Speech RecognitionCode1
Improving Self-supervised Pre-training using Accent-Specific CodebooksCode1
Pinyin Regularization in Error Correction for Chinese Speech Recognition with Large Language ModelsCode1
ArzEn-LLM: Code-Switched Egyptian Arabic-English Translation and Speech Recognition Using LLMsCode1
Automatic speech recognition for the Nepali language using CNN, bidirectional LSTM and ResNetCode1
Towards Building an End-to-End Multilingual Automatic Lyrics Transcription ModelCode1
Continual Test-time Adaptation for End-to-end Speech Recognition on Noisy SpeechCode1
Prompting Large Language Models with Audio for General-Purpose Speech SummarizationCode1
LLM-based speaker diarization correction: A generalizable approachCode1
Beyond Performance Plateaus: A Comprehensive Study on Scalability in Speech EnhancementCode1
LipGER: Visually-Conditioned Generative Error Correction for Robust Automatic Speech RecognitionCode1
Label-Synchronous Neural Transducer for E2E Simultaneous Speech TranslationCode1
A Variance-Preserving Interpolation Approach for Diffusion Models with Applications to Single Channel Speech Enhancement and RecognitionCode1
SoccerNet-Echoes: A Soccer Game Audio Commentary DatasetCode1
Watch Your Mouth: Silent Speech Recognition with Depth SensingCode1
Muting Whisper: A Universal Acoustic Adversarial Attack on Speech Foundation ModelsCode1
Unveiling the Potential of LLM-Based ASR on Chinese Open-Source DatasetsCode1
Less Peaky and More Accurate CTC Forced Alignment by Label PriorsCode1
CMULAB: An Open-Source Framework for Training and Deployment of Natural Language Processing ModelsCode1
Kallaama: A Transcribed Speech Dataset about Agriculture in the Three Most Widely Spoken Languages in SenegalCode1
FlowerFormer: Empowering Neural Architecture Encoding using a Flow-aware Graph TransformerCode1
Real-Time Multimodal Cognitive Assistant for Emergency Medical ServicesCode1
Speech Robust Bench: A Robustness Benchmark For Speech RecognitionCode1
Language and Speech Technology for Central Kurdish VarietiesCode1
A Cross-Modal Approach to Silent Speech with LLM-Enhanced RecognitionCode1
It's Never Too Late: Fusing Acoustic Information into Large Language Models for Automatic Speech RecognitionCode1
REBORN: Reinforcement-Learned Boundary Segmentation with Iterative Training for Unsupervised ASRCode1
TDFNet: An Efficient Audio-Visual Speech Separation Model with Top-down FusionCode1
Word-Level ASR Quality Estimation for Efficient Corpus Sampling and Post-Editing through Analyzing Attentions of a Reference-Free MetricCode1
Efficient Training for Multilingual Visual Speech Recognition: Pre-training with Discretized Visual Speech RepresentationCode1
Cross-Speaker Encoding Network for Multi-Talker Speech RecognitionCode1
The NPU-ASLP-LiAuto System Description for Visual Speech Recognition in CNVSRC 2023Code1
FlowMur: A Stealthy and Practical Audio Backdoor Attack with Limited KnowledgeCode1
Personalized Autonomous Driving with Large Language Models: Field ExperimentsCode1
Extending Whisper with prompt tuning to target-speaker ASRCode1
Graph Convolutions Enrich the Self-Attention in Transformers!Code1
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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