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 501550 of 6433 papers

TitleStatusHype
Gradient Remedy for Multi-Task Learning in End-to-End Noise-Robust Speech RecognitionCode1
Graph Convolutions Enrich the Self-Attention in Transformers!Code1
Towards Building an End-to-End Multilingual Automatic Lyrics Transcription ModelCode1
Towards Improved Room Impulse Response Estimation for Speech RecognitionCode1
Towards Resistant Audio Adversarial ExamplesCode1
HAPI: A Large-scale Longitudinal Dataset of Commercial ML API PredictionsCode1
Language and Speech Technology for Central Kurdish VarietiesCode1
Hearing Lips in Noise: Universal Viseme-Phoneme Mapping and Transfer for Robust Audio-Visual Speech RecognitionCode1
Interactive Feature Fusion for End-to-End Noise-Robust Speech RecognitionCode1
HiFi-VC: High Quality ASR-Based Voice ConversionCode1
Integrating Lattice-Free MMI into End-to-End Speech RecognitionCode1
Investigating the Reordering Capability in CTC-based Non-Autoregressive End-to-End Speech TranslationCode1
indic-punct: An automatic punctuation restoration and inverse text normalization framework for Indic languagesCode1
Attention-based Contextual Language Model Adaptation for Speech RecognitionCode1
IndicSUPERB: A Speech Processing Universal Performance Benchmark for Indian languagesCode1
Investigation of End-To-End Speaker-Attributed ASR for Continuous Multi-Talker RecordingsCode1
Improving Whispered Speech Recognition Performance using Pseudo-whispered based Data AugmentationCode1
Improving Vietnamese Named Entity Recognition from Speech Using Word Capitalization and Punctuation Recovery ModelsCode1
Imputer: Sequence Modelling via Imputation and Dynamic ProgrammingCode1
Improving Self-supervised Pre-training using Accent-Specific CodebooksCode1
Improving Mandarin End-to-End Speech Recognition with Word N-gram Language ModelCode1
ASR Error Correction with Constrained Decoding on Operation PredictionCode1
Improving Mandarin Speech Recogntion with Block-augmented TransformerCode1
Improving Transformer-based Speech Recognition Using Unsupervised Pre-trainingCode1
Incorporating External POS Tagger for Punctuation RestorationCode1
ÌròyìnSpeech: A multi-purpose Yorùbá Speech CorpusCode1
ASR data augmentation in low-resource settings using cross-lingual multi-speaker TTS and cross-lingual voice conversionCode1
Improved Open Source Automatic Subtitling for Lecture VideosCode1
A Study of Multilingual End-to-End Speech Recognition for Kazakh, Russian, and EnglishCode1
Improving RNN Transducer Based ASR with Auxiliary TasksCode1
Improved DeepFake Detection Using Whisper FeaturesCode1
A Survey on Non-Autoregressive Generation for Neural Machine Translation and BeyondCode1
A Systematic Comparison of Phonetic Aware Techniques for Speech EnhancementCode1
ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control CommunicationsCode1
A transfer learning based approach for pronunciation scoringCode1
An Investigation of End-to-End Models for Robust Speech RecognitionCode1
Attack on practical speaker verification system using universal adversarial perturbationsCode1
Attention-based Audio-Visual Fusion for Robust Automatic Speech RecognitionCode1
Attention-Based Models for Speech RecognitionCode1
A Sidecar Separator Can Convert a Single-Talker Speech Recognition System to a Multi-Talker OneCode1
Attention model for articulatory features detectionCode1
Attentive Sequence-to-Sequence Learning for Diacritic Restoration of Yorùbá Language TextCode1
Audio-Visual Representation Learning via Knowledge Distillation from Speech Foundation ModelsCode1
Audio-Visual Efficient Conformer for Robust Speech RecognitionCode1
Automatic Speech Recognition in Sanskrit: A New Speech Corpus and Modelling InsightsCode1
It's Never Too Late: Fusing Acoustic Information into Large Language Models for Automatic Speech RecognitionCode1
AutoDiCE: Fully Automated Distributed CNN Inference at the EdgeCode1
Improved Noisy Student Training for Automatic Speech RecognitionCode1
Improved training of end-to-end attention models for speech recognitionCode1
A Resource for Computational Experiments on MapudungunCode1
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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