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 21512200 of 6433 papers

TitleStatusHype
Towards the Transferable Audio Adversarial Attack via Ensemble Methods0
Multimodal Short Video Rumor Detection System Based on Contrastive Learning0
Political corpus creation through automatic speech recognition on EU debatesCode0
A Virtual Simulation-Pilot Agent for Training of Air Traffic Controllers0
Evaluation of Speaker Anonymization on Emotional Speech0
A CTC Alignment-based Non-autoregressive Transformer for End-to-end Automatic Speech Recognition0
Task-oriented Document-Grounded Dialog Systems by HLTPR@RWTH for DSTC9 and DSTC100
Solving Tensor Low Cycle Rank Approximation0
Speech Reconstruction from Silent Tongue and Lip Articulation By Pseudo Target Generation and Domain Adversarial Training0
Regularizing Contrastive Predictive Coding for Speech Applications0
Acoustic absement in detail: Quantifying acoustic differences across time-series representations of speech dataCode0
Wav2code: Restore Clean Speech Representations via Codebook Lookup for Noise-Robust ASR0
Sim-T: Simplify the Transformer Network by Multiplexing Technique for Speech Recognition0
Certifiable Black-Box Attacks with Randomized Adversarial Examples: Breaking Defenses with Provable ConfidenceCode0
Adaptive Feature Fusion: Enhancing Generalization in Deep Learning Models0
Scalable and Accurate Self-supervised Multimodal Representation Learning without Aligned Video and Text Data0
Dual-Attention Neural Transducers for Efficient Wake Word Spotting in Speech Recognition0
Self-Supervised Learning-Based Source Separation for Meeting Data0
Multilingual Word Error Rate Estimation: e-WER30
The Edinburgh International Accents of English Corpus: Towards the Democratization of English ASR0
Lego-Features: Exporting modular encoder features for streaming and deliberation ASR0
Dialog act guided contextual adapter for personalized speech recognition0
Improving the previous state-of-the-art Frisian ASR by fine-tuning XLS-R0
SynthVSR: Scaling Up Visual Speech Recognition With Synthetic Supervision0
PROCTER: PROnunciation-aware ConTextual adaptER for personalized speech recognition in neural transducers0
AVFormer: Injecting Vision into Frozen Speech Models for Zero-Shot AV-ASR0
Joint unsupervised and supervised learning for context-aware language identification0
Text is All You Need: Personalizing ASR Models using Controllable Speech Synthesis0
MSAT: Biologically Inspired Multi-Stage Adaptive Threshold for Conversion of Spiking Neural Networks0
Enhancing Unsupervised Speech Recognition with Diffusion GANs0
Pyramid Multi-branch Fusion DCNN with Multi-Head Self-Attention for Mandarin Speech Recognition0
Beyond Universal Transformer: block reusing with adaptor in Transformer for automatic speech recognition0
A Deliberation-based Joint Acoustic and Text Decoder0
Self-supervised Learning with Speech Modulation Dropout0
CiCo: Domain-Aware Sign Language Retrieval via Cross-Lingual Contrastive Learning0
Exploring Turkish Speech Recognition via Hybrid CTC/Attention Architecture and Multi-feature Fusion Network0
Transformers in Speech Processing: A Survey0
End-to-End Integration of Speech Separation and Voice Activity Detection for Low-Latency Diarization of Telephone Conversations0
On-the-fly Text Retrieval for End-to-End ASR Adaptation0
Knowledge Distillation from Multiple Foundation Models for End-to-End Speech Recognition0
Code-Switching Text Generation and Injection in Mandarin-English ASR0
A Deep Learning System for Domain-specific Speech Recognition0
DistillW2V2: A Small and Streaming Wav2vec 2.0 Based ASR Model0
Visual Information Matters for ASR Error Correction0
Trustera: A Live Conversation Redaction System0
Improving Perceptual Quality, Intelligibility, and Acoustics on VoIP Platforms0
Sharing Low Rank Conformer Weights for Tiny Always-On Ambient Speech Recognition Models0
HYBRIDFORMER: improving SqueezeFormer with hybrid attention and NSR mechanismCode0
A large-scale multimodal dataset of human speech recognition0
Cascading and Direct Approaches to Unsupervised Constituency Parsing on Spoken SentencesCode0
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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