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 22012250 of 6433 papers

TitleStatusHype
Mlphon: A Multifunctional Grapheme-Phoneme Conversion Tool Using Finite State TransducersCode0
Distilling the Knowledge of BERT for CTC-based ASR0
Towards Deep Learning-aided Wireless Channel Estimation and Channel State Information Feedback for 6G0
A Review of Sparse Expert Models in Deep Learning0
Semantically Meaningful Metrics for Norwegian ASR SystemsCode0
Universal Fourier Attack for Time Series0
DeepCon: An End-to-End Multilingual Toolkit for Automatic Minuting of Multi-Party Dialogues0
Robust Translation of French Live Speech Transcripts0
Evaluation of Automatic Speech Recognition for Conversational Speech in Dutch, English and German: What Goes Missing?0
Improved Open Source Automatic Subtitling for Lecture VideosCode1
A Wavelet Transform Based Scheme to Extract Speech Pitch and Formant Frequencies0
Deep Sparse Conformer for Speech RecognitionCode1
Attention Enhanced Citrinet for Speech Recognition0
RecLight: A Recurrent Neural Network Accelerator with Integrated Silicon Photonics0
Visual Speech Recognition in a Driver Assistance System0
A Language Agnostic Multilingual Streaming On-Device ASR System0
Turn-Taking Prediction for Natural Conversational Speech0
Bayesian Neural Network Language Modeling for Speech RecognitionCode0
Minimal Feature Analysis for Isolated Digit Recognition for varying encoding rates in noisy environments0
Convolutional Neural Network (CNN) to reduce construction loss in JPEG compression caused by Discrete Fourier Transform (DFT)Code1
Investigating data partitioning strategies for crosslinguistic low-resource ASR evaluation0
Effectiveness of Mining Audio and Text Pairs from Public Data for Improving ASR Systems for Low-Resource Languages0
IndicSUPERB: A Speech Processing Universal Performance Benchmark for Indian languagesCode1
Low-Level Physiological Implications of End-to-End Learning of Speech Recognition0
DualVoice: Speech Interaction that Discriminates between Normal and Whispered Voice Input0
Are disentangled representations all you need to build speaker anonymization systems?0
Analyzing Robustness of End-to-End Neural Models for Automatic Speech RecognitionCode0
Building a Public Domain Voice Database for Odia0
Uconv-Conformer: High Reduction of Input Sequence Length for End-to-End Speech Recognition0
Improving Hypernasality Estimation with Automatic Speech Recognition in Cleft Palate Speech0
Comparison and Analysis of New Curriculum Criteria for End-to-End ASRCode0
Speaker-adaptive Lip Reading with User-dependent PaddingCode0
Thai Wav2Vec2.0 with CommonVoice V8Code0
ASR Error Correction with Constrained Decoding on Operation PredictionCode1
Model Blending for Text Classification0
Large vocabulary speech recognition for languages of Africa: multilingual modeling and self-supervised learning0
Automatic Speech Recognition in German: A Detailed Error Analysis0
Adversarial Attacks on ASR Systems: An Overview0
Multiclass ASMA vs Targeted PGD Attack in Image Segmentation0
DENT-DDSP: Data-efficient noisy speech generator using differentiable digital signal processors for explicit distortion modelling and noise-robust speech recognitionCode1
Global Performance Disparities Between English-Language Accents in Automatic Speech Recognition0
Domain Specific Wav2vec 2.0 Fine-tuning For The SE&R 2022 ChallengeCode0
Multiple-hypothesis RNN-T Loss for Unsupervised Fine-tuning and Self-training of Neural Transducer0
Pronunciation-aware unique character encoding for RNN Transducer-based Mandarin speech recognition0
Thutmose Tagger: Single-pass neural model for Inverse Text Normalization0
Extending RNN-T-based speech recognition systems with emotion and language classification0
Knowledge-driven Subword Grammar Modeling for Automatic Speech Recognition in Tamil and Kannada0
Subword Dictionary Learning and Segmentation Techniques for Automatic Speech Recognition in Tamil and Kannada0
SoundChoice: Grapheme-to-Phoneme Models with Semantic Disambiguation0
Perception-Aware Attack: Creating Adversarial Music via Reverse-Engineering Human Perception0
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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