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 18511900 of 6433 papers

TitleStatusHype
Text-only domain adaptation for end-to-end ASR using integrated text-to-mel-spectrogram generator0
From Audio to Symbolic Encoding0
Speech Corpora Divergence Based Unsupervised Data Selection for ASR0
Efficient Ensemble for Multimodal Punctuation Restoration using Time-Delay Neural NetworkCode0
Chaotic Variational Auto encoder-based Adversarial Machine Learning0
Pre-Finetuning for Few-Shot Emotional Speech RecognitionCode0
Improving Massively Multilingual ASR With Auxiliary CTC Objectives0
Factual Consistency Oriented Speech Recognition0
Ensemble knowledge distillation of self-supervised speech models0
Evaluating Automatic Speech Recognition in an Incremental Setting0
Generalization of Auto-Regressive Hidden Markov Models to Non-Linear Dynamics and Unit Quaternion Observation Space0
Gradient Remedy for Multi-Task Learning in End-to-End Noise-Robust Speech RecognitionCode1
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
Connecting Humanities and Social Sciences: Applying Language and Speech Technology to Online Panel Surveys0
An ASR-free Fluency Scoring Approach with Self-Supervised Learning0
Emphasizing Unseen Words: New Vocabulary Acquisition for End-to-End Speech Recognition0
A Sidecar Separator Can Convert a Single-Talker Speech Recognition System to a Multi-Talker OneCode1
Optimization Methods in Deep Learning: A Comprehensive Overview0
Front-End Adapter: Adapting Front-End Input of Speech based Self-Supervised Learning for Speech Recognition0
Speaker and Language Change Detection using Wav2vec2 and Whisper0
Conformers are All You Need for Visual Speech Recognition0
Massively Multilingual Shallow Fusion with Large Language Models0
Measuring Equality in Machine Learning Security Defenses: A Case Study in Speech Recognition0
Audio-Visual Speech and Gesture Recognition by Sensors of Mobile Devices0
Stabilising and accelerating light gated recurrent units for automatic speech recognition0
Speaker Change Detection for Transformer Transducer ASR0
Adaptive Axonal Delays in feedforward spiking neural networks for accurate spoken word recognition0
Adaptable End-to-End ASR Models using Replaceable Internal LMs and Residual Softmax0
JEIT: Joint End-to-End Model and Internal Language Model Training for Speech Recognition0
Prompt Tuning of Deep Neural Networks for Speaker-adaptive Visual Speech Recognition0
Confidence Score Based Speaker Adaptation of Conformer Speech Recognition SystemsCode0
READIN: A Chinese Multi-Task Benchmark with Realistic and Diverse Input NoisesCode0
Sneaky Spikes: Uncovering Stealthy Backdoor Attacks in Spiking Neural Networks with Neuromorphic DataCode0
ASR Bundestag: A Large-Scale political debate dataset in German0
ASDF: A Differential Testing Framework for Automatic Speech Recognition SystemsCode0
AV-data2vec: Self-supervised Learning of Audio-Visual Speech Representations with Contextualized Target Representations0
PATCorrect: Non-autoregressive Phoneme-augmented Transformer for ASR Error Correction0
Leveraging supplementary text data to kick-start automatic speech recognition system development with limited transcriptions0
LUT-NN: Empower Efficient Neural Network Inference with Centroid Learning and Table Lookup0
MAC: A unified framework boosting low resource automatic speech recognition0
Efficient Domain Adaptation for Speech Foundation Models0
Complex Dynamic Neurons Improved Spiking Transformer Network for Efficient Automatic Speech RecognitionCode1
Improving Rare Words Recognition through Homophone Extension and Unified Writing for Low-resource Cantonese Speech Recognition0
Knowledge Transfer from Pre-trained Language Models to Cif-based Speech Recognizers via Hierarchical DistillationCode1
Exploring Attention Map Reuse for Efficient Transformer Neural Networks0
Fillers in Spoken Language Understanding: Computational and Psycholinguistic Perspectives0
A Comparison of Temporal Encoders for Neuromorphic Keyword Spotting with Few Neurons0
Unsupervised Data Selection for TTS: Using Arabic Broadcast News as a Case StudyCode0
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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