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
Dynamic Alignment Mask CTC: Improved Mask-CTC with Aligned Cross Entropy0
Improving Accented Speech Recognition with Multi-Domain Training0
I3D: Transformer architectures with input-dependent dynamic depth for speech recognitionCode0
Context-Aware Selective Label Smoothing for Calibrating Sequence Recognition Model0
Fine-tuning Strategies for Faster Inference using Speech Self-Supervised Models: A Comparative StudyCode0
Improving the Intent Classification accuracy in Noisy Environment0
Transcription free filler word detection with Neural semi-CRFsCode0
The NPU-ASLP System for Audio-Visual Speech Recognition in MISP 2022 Challenge0
MIXPGD: Hybrid Adversarial Training for Speech Recognition Systems0
Clinical BERTScore: An Improved Measure of Automatic Speech Recognition Performance in Clinical Settings0
An Overview on Language Models: Recent Developments and Outlook0
Unsupervised Language agnostic WER Standardization0
DeepGD: A Multi-Objective Black-Box Test Selection Approach for Deep Neural NetworksCode0
wav2vec and its current potential to Automatic Speech Recognition in German for the usage in Digital History: A comparative assessment of available ASR-technologies for the use in cultural heritage contexts0
SottoVoce: An Ultrasound Imaging-Based Silent Speech Interaction Using Deep Neural Networks0
Pre-trained Model Representations and their Robustness against Noise for Speech Emotion Analysis0
End-to-End Speech Recognition: A Survey0
LiteG2P: A fast, light and high accuracy model for grapheme-to-phoneme conversion0
Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages0
Leveraging Large Text Corpora for End-to-End Speech Summarization0
Synthetic Cross-accent Data Augmentation for Automatic Speech Recognition0
N-best T5: Robust ASR Error Correction using Multiple Input Hypotheses and Constrained Decoding Space0
Leveraging Redundancy in Multiple Audio Signals for Far-Field Speech Recognition0
Practice of the conformer enhanced AUDIO-VISUAL HUBERT on Mandarin and English0
Exploring Self-supervised Pre-trained ASR Models For Dysarthric and Elderly Speech Recognition0
Language-Universal Adapter Learning with Knowledge Distillation for End-to-End Multilingual Speech RecognitionCode0
A Token-Wise Beam Search Algorithm for RNN-T0
A Comparison of Speech Data Augmentation Methods Using S3PRL Toolkit0
Diagonal State Space Augmented Transformers for Speech Recognition0
Diacritic Recognition Performance in Arabic ASR0
Deep Visual Forced Alignment: Learning to Align Transcription with Talking Face Video0
MoLE : Mixture of Language Experts for Multi-Lingual Automatic Speech Recognition0
A low latency attention module for streaming self-supervised speech representation learningCode0
Explanations for Automatic Speech Recognition0
Multimodal Speech Recognition for Language-Guided Embodied AgentsCode0
Improving Medical Speech-to-Text Accuracy with Vision-Language Pre-training Model0
Text-only domain adaptation for end-to-end ASR using integrated text-to-mel-spectrogram generator0
Efficient Ensemble for Multimodal Punctuation Restoration using Time-Delay Neural NetworkCode0
Speech Corpora Divergence Based Unsupervised Data Selection for ASR0
From Audio to Symbolic Encoding0
Chaotic Variational Auto encoder-based Adversarial Machine Learning0
Ensemble knowledge distillation of self-supervised speech models0
Pre-Finetuning for Few-Shot Emotional Speech RecognitionCode0
Improving Massively Multilingual ASR With Auxiliary CTC Objectives0
Factual Consistency Oriented Speech Recognition0
Generalization of Auto-Regressive Hidden Markov Models to Non-Linear Dynamics and Unit Quaternion Observation Space0
Evaluating Automatic Speech Recognition in an Incremental Setting0
Improving Contextual Spelling Correction by External Acoustics Attention and Semantic Aware Data Augmentation0
MADI: Inter-domain Matching and Intra-domain Discrimination for Cross-domain Speech Recognition0
UML: A Universal Monolingual Output Layer for Multilingual ASR0
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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