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 251300 of 6433 papers

TitleStatusHype
STHG: Spatial-Temporal Heterogeneous Graph Learning for Advanced Audio-Visual DiarizationCode1
Pushing the Limits of Unsupervised Unit Discovery for SSL Speech RepresentationCode1
Utilizing Longitudinal Chest X-Rays and Reports to Pre-Fill Radiology ReportsCode1
ITALIC: An Italian Intent Classification DatasetCode1
Contrastive Learning-Based Audio to Lyrics Alignment for Multiple LanguagesCode1
OpenSR: Open-Modality Speech Recognition via Maintaining Multi-Modality AlignmentCode1
Zambezi Voice: A Multilingual Speech Corpus for Zambian LanguagesCode1
Allophant: Cross-lingual Phoneme Recognition with Articulatory AttributesCode1
MAVD: The First Open Large-Scale Mandarin Audio-Visual Dataset with Depth InformationCode1
SGEM: Test-Time Adaptation for Automatic Speech Recognition via Sequential-Level Generalized Entropy MinimizationCode1
Can Contextual Biasing Remain Effective with Whisper and GPT-2?Code1
Improved DeepFake Detection Using Whisper FeaturesCode1
DistilXLSR: A Light Weight Cross-Lingual Speech Representation ModelCode1
Perception and Semantic Aware Regularization for Sequential Confidence CalibrationCode1
Bridging the Granularity Gap for Acoustic ModelingCode1
BIG-C: a Multimodal Multi-Purpose Dataset for BembaCode1
Scaling Speech Technology to 1,000+ LanguagesCode1
CopyNE: Better Contextual ASR by Copying Named EntitiesCode1
Making More of Little Data: Improving Low-Resource Automatic Speech Recognition Using Data AugmentationCode1
Prompting the Hidden Talent of Web-Scale Speech Models for Zero-Shot Task GeneralizationCode1
Self-supervised Fine-tuning for Improved Content Representations by Speaker-invariant ClusteringCode1
Cross-Modal Global Interaction and Local Alignment for Audio-Visual Speech RecognitionCode1
Back Translation for Speech-to-text Translation Without TranscriptsCode1
CB-Conformer: Contextual biasing Conformer for biased word recognitionCode1
Efficient Sequence Transduction by Jointly Predicting Tokens and DurationsCode1
When Good and Reproducible Results are a Giant with Feet of Clay: The Importance of Software Quality in NLPCode1
Watch or Listen: Robust Audio-Visual Speech Recognition with Visual Corruption Modeling and Reliability ScoringCode1
TS-SEP: Joint Diarization and Separation Conditioned on Estimated Speaker EmbeddingsCode1
Calibrating Transformers via Sparse Gaussian ProcessesCode1
BrainBERT: Self-supervised representation learning for intracranial recordingsCode1
Structured Pruning of Self-Supervised Pre-trained Models for Speech Recognition and UnderstandingCode1
Gradient Remedy for Multi-Task Learning in End-to-End Noise-Robust Speech RecognitionCode1
A Sidecar Separator Can Convert a Single-Talker Speech Recognition System to a Multi-Talker OneCode1
Complex Dynamic Neurons Improved Spiking Transformer Network for Efficient Automatic Speech RecognitionCode1
Knowledge Transfer from Pre-trained Language Models to Cif-based Speech Recognizers via Hierarchical DistillationCode1
OLKAVS: An Open Large-Scale Korean Audio-Visual Speech DatasetCode1
Audio-Visual Efficient Conformer for Robust Speech RecognitionCode1
Towards Voice Reconstruction from EEG during Imagined SpeechCode1
Learning to Detect Noisy Labels Using Model-Based FeaturesCode1
Skit-S2I: An Indian Accented Speech to Intent datasetCode1
Efficient Self-supervised Learning with Contextualized Target Representations for Vision, Speech and LanguageCode1
Jointly Learning Visual and Auditory Speech Representations from Raw DataCode1
BASPRO: a balanced script producer for speech corpus collection based on the genetic algorithmCode1
GPU-accelerated Guided Source Separation for Meeting TranscriptionCode1
SoftCTC -- Semi-Supervised Learning for Text Recognition using Soft Pseudo-LabelsCode1
On Word Error Rate Definitions and their Efficient Computation for Multi-Speaker Speech Recognition SystemsCode1
A Persian ASR-based SER: Modification of Sharif Emotional Speech Database and Investigation of Persian Text CorporaCode1
MelHuBERT: A simplified HuBERT on Mel spectrogramsCode1
MT4SSL: Boosting Self-Supervised Speech Representation Learning by Integrating Multiple TargetsCode1
ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control CommunicationsCode1
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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
9Gated ConvNetsWord Error Rate (WER)4.8Unverified
10HMM-TDNN + iVectorsWord 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
7HMM-TDNN + pNorm + speed up/down speechPercentage error12.9Unverified
8DNN MPEPercentage error12.9Unverified
9DNN MMIPercentage error12.9Unverified
10CNN + Bi-RNN + CTC (speech to letters), 25.9% WER if trainedonlyon SWBPercentage 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
6TC-DNN-BLSTM-DNNWord Error Rate (WER)3.5Unverified
7Convolutional Speech RecognitionWord 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