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 46514700 of 6433 papers

TitleStatusHype
A Hardware-Oriented and Memory-Efficient Method for CTC Decoding0
Learning Optimal Data Augmentation Policies via Bayesian Optimization for Image Classification TasksCode0
Meeting Transcription Using Virtual Microphone Arrays0
Parity Models: A General Framework for Coding-Based Resilience in ML Inference0
Curvature: A signature for Action Recognition in Video Sequences0
English Broadcast News Speech Recognition by Humans and Machines0
Very Deep Self-Attention Networks for End-to-End Speech Recognition0
Deep Learning for Audio Signal ProcessingCode0
Semi-supervised Sequence-to-sequence ASR using Unpaired Speech and Text0
Adversarial Speaker Adaptation0
Frequency Domain Multi-channel Acoustic Modeling for Distant Speech Recognition0
Multi-Geometry Spatial Acoustic Modeling for Distant Speech Recognition0
Attentive Adversarial Learning for Domain-Invariant Training0
Assessing the Tolerance of Neural Machine Translation Systems Against Speech Recognition Errors0
Phonetically-Oriented Word Error Alignment for Speech Recognition Error Analysis in Speech TranslationCode0
Realizing Petabyte Scale Acoustic ModelingCode0
Natural Language Interactions in Autonomous Vehicles: Intent Detection and Slot Filling from Passenger Utterances0
NLP Driven Ensemble Based Automatic Subtitle Generation and Semantic Video Summarization Technique0
A Novel Task-Oriented Text Corpus in Silent Speech Recognition and its Natural Language Generation Construction Method0
An Investigation of End-to-End Multichannel Speech Recognition for Reverberant and Mismatch Conditions0
TTS Skins: Speaker Conversion via ASR0
The Speechtransformer for Large-scale Mandarin Chinese Speech Recognition0
End-to-End Speech Translation with Knowledge Distillation0
Guiding CTC Posterior Spike Timings for Improved Posterior Fusion and Knowledge Distillation0
Hard Sample Mining for the Improved Retraining of Automatic Speech Recognition0
A Multi-Task Learning Framework for Overcoming the Catastrophic Forgetting in Automatic Speech Recognition0
HARK Side of Deep Learning -- From Grad Student Descent to Automated Machine Learning0
CRF-based Single-stage Acoustic Modeling with CTC Topology0
Attention-Passing Models for Robust and Data-Efficient End-to-End Speech Translation0
SpeechYOLO: Detection and Localization of Speech Objects0
Low-Latency Speaker-Independent Continuous Speech Separation0
STC Speaker Recognition Systems for the VOiCES From a Distance Challenge0
Unsupervised Speech Domain Adaptation Based on Disentangled Representation Learning for Robust Speech RecognitionCode0
From Semi-supervised to Almost-unsupervised Speech Recognition with Very-low Resource by Jointly Learning Phonetic Structures from Audio and Text Embeddings0
Distributed Deep Learning Strategies For Automatic Speech Recognition0
Deep Cytometry: Deep learning with Real-time Inference in Cell Sorting and Flow Cytometry0
Who Needs Words? Lexicon-Free Speech Recognition0
Performance Monitoring for End-to-End Speech Recognition0
Exploring Methods for the Automatic Detection of Errors in Manual Transcription0
Completely Unsupervised Speech Recognition By A Generative Adversarial Network Harmonized With Iteratively Refined Hidden Markov Models0
SPEAK YOUR MIND! Towards Imagined Speech Recognition With Hierarchical Deep Learning0
Knowledge Distillation For Recurrent Neural Network Language Modeling With Trust Regularization0
A Target-Agnostic Attack on Deep Models: Exploiting Security Vulnerabilities of Transfer LearningCode0
Constrained Output Embeddings for End-to-End Code-Switching Speech Recognition with Only Monolingual Data0
Enriching Rare Word Representations in Neural Language Models by Embedding Matrix AugmentationCode0
Spoken Language Intent Detection using Confusion2VecCode0
Token-Level Ensemble Distillation for Grapheme-to-Phoneme Conversion0
Jasper: An End-to-End Convolutional Neural Acoustic ModelCode0
Sequence-to-Sequence Speech Recognition with Time-Depth Separable Convolutions0
Massively Multilingual Adversarial 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