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A Near Real-Time Automatic Speaker Recognition Architecture for Voice-Based User Interface

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Gabor Filterbank Features for Robust Speech Recognition
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Effects of Voice-Based Synthetic Assistant on Performance of Emergency Care Provider in Training
inferredcites →https://doi.org/10.1016/0167-6393(90)90010-7openalex-snapshot
Speech database development at MIT: Timit and beyond
inferredcites →https://doi.org/10.1016/j.csl.2017.02.006openalex-snapshot
On the relevance of auditory-based Gabor features for deep learning in robust speech recognition
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CloudID: Trustworthy cloud-based and cross-enterprise biometric identification
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Audio Feature Extraction and Analysis for Scene Segmentation and Classification
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Comparative study of automatic speech recognition techniques
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Common Metrics to Benchmark Human-Machine Teams (HMT): A Review
inferredcites →https://doi.org/10.1109/asru.2011.6163903openalex-snapshot
Convolutive Bottleneck Network features for LVCSR
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Age and gender classification using convolutional neural networks
inferredcites →https://doi.org/10.1109/icassp.2013.6639347openalex-snapshot
Deep convolutional neural networks for LVCSR
inferredcites →https://doi.org/10.1109/icassp.2019.8683892openalex-snapshot
Fully Supervised Speaker Diarization
inferredcites →https://doi.org/10.1109/icimtr.2012.6236471openalex-snapshot
Quantized histogram color features analysis for image retrieval based on median and Laplacian filters in DCT domain
inferredcites →https://doi.org/10.1109/icsmc.1990.142050openalex-snapshot
Unsupervised texture segmentation using Gabor filters
inferredcites →https://doi.org/10.1109/islped.2017.8009202openalex-snapshot
Gabor filter assisted energy efficient fast learning Convolutional Neural Networks
inferredcites →https://doi.org/10.1109/isspit.2018.8642755openalex-snapshot
Detection and Identification of Background Sounds to Improvise Voice Interface in Critical Environments
inferredcites →https://doi.org/10.1109/jstsp.2014.2364559openalex-snapshot
A Real-Time End-to-End Multilingual Speech Recognition Architecture
inferredcites →https://doi.org/10.1109/msp.2012.2205597openalex-snapshot
Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups
inferredcites →https://doi.org/10.1109/taslp.2014.2339736openalex-snapshot
Convolutional Neural Networks for Speech Recognition
inferredcites →https://doi.org/10.1109/tii.2018.2799928openalex-snapshot
GMM and CNN Hybrid Method for Short Utterance Speaker Recognition
inferredcites →https://doi.org/10.1109/tmi.2002.806569openalex-snapshot
A support vector machine approach for detection of microcalcifications
inferredcites →https://doi.org/10.1117/1.jrs.11.015020openalex-snapshot
Graph-based approach for airborne light detection and ranging segmentation
inferredcites →https://doi.org/10.1136/bmj.i2139openalex-snapshot
Medical error—the third leading cause of death in the US
inferredcites →https://doi.org/10.1145/3065386openalex-snapshot
ImageNet classification with deep convolutional neural networks
inferredcites →https://doi.org/10.1155/2015/209814openalex-snapshot
Optimized Audio Classification and Segmentation Algorithm by Using Ensemble Methods
inferredcites →https://doi.org/10.14257/ijsip.2016.9.4.34openalex-snapshot
A Review on Automatic Speech Recognition Architecture and Approaches
inferredcites →https://doi.org/10.14445/22315381/ijett-v10p298openalex-snapshot
An Outdoor Navigation With Voice Recognition Security Application For Visually Impaired People
inferredcites →https://doi.org/10.17485/ijst/2016/v9i4/83894openalex-snapshot
A Voice Identification System using Hidden Markov Model
inferredcites →https://doi.org/10.17485/ijst/2017/v10i19/115518openalex-snapshot
Issues and Challenges of Voice Recognition in Pervasive Environment
inferredcites →https://doi.org/10.18260/1-2--22461openalex-snapshot
Software/Hardware Implementation of an Adaptive Noise Cancellation System
inferredcites →https://doi.org/10.18653/v1/d15-1303openalex-snapshot
Deep Convolutional Neural Network Textual Features and Multiple Kernel Learning for Utterance-level Multimodal Sentiment Analysis
inferredcites →https://doi.org/10.21437/interspeech.2013-46openalex-snapshot
Informative spectro-temporal bottleneck features for noise-robust speech recognition
inferredcites →https://doi.org/10.21437/interspeech.2014-226openalex-snapshot
Robust CNN-based speech recognition with Gabor filter kernels
inferredcites →https://doi.org/10.21437/interspeech.2017-950openalex-snapshot
VoxCeleb: A Large-Scale Speaker Identification Dataset
inferredcites →https://doi.org/10.21437/interspeech.2018-1929openalex-snapshot
VoxCeleb2: Deep Speaker Recognition
inferredcites →https://doi.org/10.48550/arxiv.1201.0490openalex-snapshot
Scikit-learn: Machine Learning in Python
inferredcites →https://doi.org/10.48550/arxiv.1207.0580openalex-snapshot
Improving neural networks by preventing co-adaptation of feature detectors
inferredcites →https://doi.org/10.48550/arxiv.1306.0239openalex-snapshot
Deep Learning using Linear Support Vector Machines
inferredcites →https://doi.org/10.48550/arxiv.1706.09559openalex-snapshot
Audio Spectrogram Representations for Processing with Convolutional Neural Networks
inferredcites →https://doi.org/10.5120/4870-7297openalex-snapshot
Voice Recognition and Authentication as a Proficient Biometric Tool and Its Application in Online Exam for P.H People
inferredcites →https://doi.org/10.5555/2627435.2697065openalex-snapshot
Do we need hundreds of classifiers to solve real world classification problems
inferredcites →https://doi.org/10.5555/2946645.3007063openalex-snapshot
Are random forests truly the best classifiers

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Audio and Visual Speech for the Biometric Security System
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Spin-Image Descriptors for Text-Independent Speaker Recognition
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Sentiment Analysis in Speech Recognition System Using Artificial Intelligence
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Attention-based multi dimension fused-feature convolutional neural network framework for speaker recognition
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Speaker Recognition using Random Forest
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Optimising gender identification from voice data by using random forest and comparing with Naive Baye’s algorithm
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