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

Showing 63516400 of 6433 papers

TitleStatusHype
Streaming Joint Speech Recognition and Disfluency DetectionCode0
CAT: CRF-based ASR ToolkitCode0
End-to-end ASR: from Supervised to Semi-Supervised Learning with Modern ArchitecturesCode0
Adaptive Cascading Network for Continual Test-Time AdaptationCode0
Open Source German Distant Speech Recognition: Corpus and Acoustic ModelCode0
Transforming faces into video stories -- VideoFace2.0Code0
SkinAugment: Auto-Encoding Speaker Conversions for Automatic Speech TranslationCode0
Emotional Speech Recognition with Pre-trained Deep Visual ModelsCode0
A Deep Dive into the Disparity of Word Error Rates Across Thousands of NPTEL MOOC VideosCode0
Cascading and Direct Approaches to Unsupervised Constituency Parsing on Spoken SentencesCode0
Convolutional Neural Network Language ModelsCode0
Adapting the adapters for code-switching in multilingual ASRCode0
Optimal Completion Distillation for Sequence LearningCode0
Fine-Grained Grounding for Multimodal Speech RecognitionCode0
Attention-based Multi-hypothesis Fusion for Speech SummarizationCode0
Voices Unheard: NLP Resources and Models for Yorùbá Regional DialectsCode0
A Morphology-aware Network for Morphological DisambiguationCode0
Rethinking Evaluation in ASR: Are Our Models Robust Enough?Code0
Federating Dynamic Models using Early-Exit Architectures for Automatic Speech Recognition on Heterogeneous ClientsCode0
Malware Makeover: Breaking ML-based Static Analysis by Modifying Executable BytesCode0
Benchmark of Deep Learning Models on Large Healthcare MIMIC DatasetsCode0
SlothSpeech: Denial-of-service Attack Against Speech Recognition ModelsCode0
Optimized Speculative Sampling for GPU Hardware AcceleratorsCode0
Streaming Sequence Transduction through Dynamic CompressionCode0
Federated Learning in ASR: Not as Easy as You ThinkCode0
Mispronunciation detection using self-supervised speech representationsCode0
Convolutional Neural Network for Paraphrase IdentificationCode0
Unsupervised Learning of Disentangled and Interpretable Representations from Sequential DataCode0
Re-Translation Strategies For Long Form, Simultaneous, Spoken Language TranslationCode0
Optimizing Deep Learning Models For Raspberry PiCode0
Fast-Slow Recurrent Neural NetworksCode0
Cascaded Cross-Modal Transformer for Audio-Textual ClassificationCode0
Mixat: A Data Set of Bilingual Emirati-English SpeechCode0
Towards End-to-End Speech Recognition with Deep Convolutional Neural NetworksCode0
A Model for Every User and Budget: Label-Free and Personalized Mixed-Precision QuantizationCode0
Mixed-Precision Training for NLP and Speech Recognition with OpenSeq2SeqCode0
Advancing African-Accented Speech Recognition: Epistemic Uncertainty-Driven Data Selection for Generalizable ASR ModelsCode0
Towards End-to-End Training of Automatic Speech Recognition for Nigerian PidginCode0
MixRep: Hidden Representation Mixup for Low-Resource Speech RecognitionCode0
Attention-Based Models for Text-Dependent Speaker VerificationCode0
Optimus: An Efficient Dynamic Resource Scheduler for Deep Learning ClustersCode0
Adaptation Algorithms for Neural Network-Based Speech Recognition: An OverviewCode0
DiaCorrect: End-to-end error correction for speaker diarizationCode0
Revealing and Protecting Labels in Distributed TrainingCode0
Orthographic Transliteration for Kabyle Speech RecognitionCode0
ELITR-Bench: A Meeting Assistant Benchmark for Long-Context Language ModelsCode0
Careless Whisper: Speech-to-Text Hallucination HarmsCode0
AdaCS: Adaptive Normalization for Enhanced Code-Switching ASRCode0
Revise, Reason, and Recognize: LLM-Based Emotion Recognition via Emotion-Specific Prompts and ASR Error CorrectionCode0
Calibrated Structured PredictionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AmNetWord Error Rate (WER)8.6Unverified
2HMM-(SAT)GMMWord Error Rate (WER)8Unverified
3Local Prior Matching (Large Model)Word Error Rate (WER)7.19Unverified
4SnipsWord Error Rate (WER)6.4Unverified
5Li-GRUWord Error Rate (WER)6.2Unverified
6HMM-DNN + pNorm*Word Error Rate (WER)5.5Unverified
7CTC + policy learningWord Error Rate (WER)5.42Unverified
8Deep Speech 2Word Error Rate (WER)5.33Unverified
9HMM-TDNN + iVectorsWord Error Rate (WER)4.8Unverified
10Gated ConvNetsWord Error Rate (WER)4.8Unverified
#ModelMetricClaimedVerifiedStatus
1Local Prior Matching (Large Model)Word Error Rate (WER)20.84Unverified
2SnipsWord Error Rate (WER)16.5Unverified
3Local Prior Matching (Large Model, ConvLM LM)Word Error Rate (WER)15.28Unverified
4Deep Speech 2Word Error Rate (WER)13.25Unverified
5TDNN + pNorm + speed up/down speechWord Error Rate (WER)12.5Unverified
6CTC-CRF 4gram-LMWord Error Rate (WER)10.65Unverified
7Convolutional Speech RecognitionWord Error Rate (WER)10.47Unverified
8MT4SSLWord Error Rate (WER)9.6Unverified
9Jasper DR 10x5Word Error Rate (WER)8.79Unverified
10EspressoWord Error Rate (WER)8.7Unverified
#ModelMetricClaimedVerifiedStatus
1Deep SpeechPercentage error20Unverified
2DNN-HMMPercentage error18.5Unverified
3CD-DNNPercentage error16.1Unverified
4DNNPercentage error16Unverified
5DNN + DropoutPercentage error15Unverified
6DNN BMMIPercentage error12.9Unverified
7DNN MPEPercentage error12.9Unverified
8DNN MMIPercentage error12.9Unverified
9HMM-TDNN + pNorm + speed up/down speechPercentage error12.9Unverified
10HMM-DNN +sMBRPercentage error12.6Unverified
#ModelMetricClaimedVerifiedStatus
1LSNNPercentage error33.2Unverified
2LAS multitask with indicators samplingPercentage error20.4Unverified
3Soft Monotonic Attention (ours, offline)Percentage error20.1Unverified
4QCNN-10L-256FMPercentage error19.64Unverified
5Bi-LSTM + skip connections w/ CTCPercentage error17.7Unverified
6Bi-RNN + AttentionPercentage error17.6Unverified
7RNN-CRF on 24(x3) MFSCPercentage error17.3Unverified
8CNN in time and frequency + dropout, 17.6% w/o dropoutPercentage error16.7Unverified
9Light Gated Recurrent UnitsPercentage error16.7Unverified
10GRUPercentage error16.6Unverified
#ModelMetricClaimedVerifiedStatus
1AttWord Error Rate (WER)18.7Unverified
2CTC/AttWord Error Rate (WER)6.7Unverified
3BRA-EWord Error Rate (WER)6.63Unverified
4CTC-CRF 4gram-LMWord Error Rate (WER)6.34Unverified
5BATWord Error Rate (WER)4.97Unverified
6ParaformerWord Error Rate (WER)4.95Unverified
7U2Word Error Rate (WER)4.72Unverified
8UMAWord Error Rate (WER)4.7Unverified
9Lightweight TransducerWord Error Rate (WER)4.31Unverified
10CIF-HKD With LMWord Error Rate (WER)4.1Unverified
#ModelMetricClaimedVerifiedStatus
1Jasper 10x3Word Error Rate (WER)6.9Unverified
2CNN over RAW speech (wav)Word Error Rate (WER)5.6Unverified
3CTC-CRF 4gram-LMWord Error Rate (WER)3.79Unverified
4Deep Speech 2Word Error Rate (WER)3.6Unverified
5test-set on open vocabulary (i.e. harder), model = HMM-DNN + pNorm*Word Error Rate (WER)3.6Unverified
6Convolutional Speech RecognitionWord Error Rate (WER)3.5Unverified
7TC-DNN-BLSTM-DNNWord Error Rate (WER)3.5Unverified
8EspressoWord Error Rate (WER)3.4Unverified
9CTC-CRF VGG-BLSTMWord Error Rate (WER)3.2Unverified
10Transformer with Relaxed AttentionWord Error Rate (WER)3.19Unverified