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 35513600 of 6433 papers

TitleStatusHype
Parallel Rescoring with Transformer for Streaming On-Device Speech Recognition0
Parameter-efficient Adaptation of Multilingual Multimodal Models for Low-resource ASR0
Parameter-Efficient Conformers via Sharing Sparsely-Gated Experts for End-to-End Speech Recognition0
Parameter-efficient Dysarthric Speech Recognition Using Adapter Fusion and Householder Transformation0
Parameter-Efficient Transfer Learning under Federated Learning for Automatic Speech Recognition0
Parity Models: A General Framework for Coding-Based Resilience in ML Inference0
ParlamentParla: A Speech Corpus of Catalan Parliamentary Sessions0
ParlaSpeech-HR - a Freely Available ASR Dataset for Croatian Bootstrapped from the ParlaMint Corpus0
PARP: Prune, Adjust and Re-Prune for Self-Supervised Speech Recognition0
Part-based Lipreading for Audio-Visual Speech Recognition0
Partial Variable Training for Efficient On-Device Federated Learning0
Partitioning Large Scale Deep Belief Networks Using Dropout0
Part-of-Speech Tagger for Bodo Language using Deep Learning approach0
PATCorrect: Non-autoregressive Phoneme-augmented Transformer for ASR Error Correction0
Pay Better Attention to Attention: Head Selection in Multilingual and Multi-Domain Sequence Modeling0
PDAugment: Data Augmentation by Pitch and Duration Adjustments for Automatic Lyrics Transcription0
Perceiver-Prompt: Flexible Speaker Adaptation in Whisper for Chinese Disordered Speech Recognition0
Perception-Aware Attack: Creating Adversarial Music via Reverse-Engineering Human Perception0
Perception of Phonological Assimilation by Neural Speech Recognition Models0
Perception Point: Identifying Critical Learning Periods in Speech for Bilingual Networks0
Perceptual-based deep-learning denoiser as a defense against adversarial attacks on ASR systems0
AeGAN: Time-Frequency Speech Denoising via Generative Adversarial Networks0
Perfect match: Improved cross-modal embeddings for audio-visual synchronisation0
Performance Analysis of Speech Encoders for Low-Resource SLU and ASR in Tunisian Dialect0
Global Performance Disparities Between English-Language Accents in Automatic Speech Recognition0
Performance Evaluation of Deep Convolutional Maxout Neural Network in Speech Recognition0
Performance Monitoring for End-to-End Speech Recognition0
Performant ASR Models for Medical Entities in Accented Speech0
The Recognition Of Persian Phonemes Using PPNet0
Persistent Hidden States and Nonlinear Transformation for Long Short-Term Memory0
Persistent Laplacian-enhanced Algorithm for Scarcely Labeled Data Classification0
persoDA: Personalized Data Augmentation for Personalized ASR0
Personalization for BERT-based Discriminative Speech Recognition Rescoring0
Personalization of CTC-based End-to-End Speech Recognition Using Pronunciation-Driven Subword Tokenization0
Towards Personalization of CTC Speech Recognition Models with Contextual Adapters and Adaptive Boosting0
Personalization of End-to-end Speech Recognition On Mobile Devices For Named Entities0
Personalization Strategies for End-to-End Speech Recognition Systems0
Personalized Adversarial Data Augmentation for Dysarthric and Elderly Speech Recognition0
Personalized Automatic Speech Recognition Trained on Small Disordered Speech Datasets0
Personalized Keyphrase Detection using Speaker and Environment Information0
Personalized Machine Translation: Predicting Translational Preferences0
Personalized Predictive ASR for Latency Reduction in Voice Assistants0
Personalized Query Rewriting in Conversational AI Agents0
Personalized Speech Enhancement: New Models and Comprehensive Evaluation0
Personalized Speech Recognition for Children with Test-Time Adaptation0
Personalized Speech recognition on mobile devices0
DITTO: Data-efficient and Fair Targeted Subset Selection for ASR Accent Adaptation0
Personal VAD 2.0: Optimizing Personal Voice Activity Detection for On-Device Speech Recognition0
PhaseFool: Phase-oriented Audio Adversarial Examples via Energy Dissipation0
PhasePerturbation: Speech Data Augmentation via Phase Perturbation for Automatic Speech Recognition0
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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