Author Info
Name
Affiliation
Papers
SABATO MARCO SINISCALCHI
Faculty of Engineering and Architecture, Kore University of Enna, Cittadella Universitaria, Enna, Sicily, Italy
69
Collaborators
Citations 
PageRank 
120
310
30.21
Referers 
Referees 
References 
641
1045
899
Search Limit
1001000
Title
Citations
PageRank
Year
A Variational Bayesian Approach to Learning Latent Variables for Acoustic Knowledge Transfer00.342022
Acoustic-to-Articulatory Mapping With Joint Optimization of Deep Speech Enhancement and Articulatory Inversion Models00.342022
A TWO-STAGE DEEP MODELING APPROACH TO ARTICULATORY INVERSION00.342021
Vector-to-Vector Regression via Distributional Loss for Speech Enhancement00.342021
A TWO-STAGE APPROACH TO DEVICE-ROBUST ACOUSTIC SCENE CLASSIFICATION00.342021
A Dnn Based Speech Enhancement Approach To Noise Robust Acoustic-To-Articulatory Inversion00.342021
PATE-AAE - Incorporating Adversarial Autoencoder into Private Aggregation of Teacher Ensembles for Spoken Command Classification.00.342021
DECENTRALIZING FEATURE EXTRACTION WITH QUANTUM CONVOLUTIONAL NEURAL NETWORK FOR AUTOMATIC SPEECH RECOGNITION10.352021
Increasing Naturalness and Flexibility in Spoken Dialogue Interaction - 10th International Workshop on Spoken Dialogue Systems, IWSDS 2019, Syracuse, Sicily, Italy, 24-26 April 2019.00.342021
Ensemble Hierarchical Extreme Learning Machine for Speech Dereverberation20.382020
Exploring Deep Hybrid Tensor-to-Vector Network Architectures for Regression Based Speech Enhancement10.352020
On Mean Absolute Error For Deep Neural Network Based Vector-To-Vector Regression10.352020
An Acoustic Segment Model Based Segment Unit Selection Approach to Acoustic Scene Classification with Partial Utterances00.342020
Transfer Learning of Articulatory Information Through Phone Information.00.342020
Performance Analysis for Tensor-Train Decomposition to Deep Neural Network Based Vector-to-Vector Regression00.342020
Maximal Figure-of-Merit Framework to Detect Multi-Label Phonetic Features for Spoken Language Recognition.10.352020
Sequence-to-Sequence Articulatory Inversion Through Time Convolution of Sub-Band Frequency Signals.00.342020
Relational Teacher Student Learning with Neural Label Embedding for Device Adaptation in Acoustic Scene Classification00.342020
Audio-Visual Speech Enhancement Using Hierarchical Extreme Learning Machine00.342019
Exploring Retraining-Free Speech Recognition For Intra-Sentential Code-Switching00.342019
Improving Mispronunciation Detection of Mandarin Tones for Non-Native Learners With Soft-Target Tone Labels and BLSTM-Based Deep Tone Models00.342019
A Theory on Deep Neural Network Based Vector-to-Vector Regression With an Illustration of Its Expressive Power in Speech Enhancement20.412019
A Phonetic-Level Analysis of Different Input Features for Articulatory Inversion00.342019
Improving Audio-Visual Speech Recognition Performance With Cross-Modal Student-Teacher Training00.342019
Bone-Conducted Speech Enhancement Using Hierarchical Extreme Learning Machine.00.342019
Compressed Multimodal Hierarchical Extreme Learning Machine for Speech Enhancement00.342019
Improving Mandarin Tone Recognition Based on DNN by Combining Acoustic and Articulatory Features Using Extended Recognition Networks.00.342018
An End-to-End Deep Learning Approach to Simultaneous Speech Dereverberation and Acoustic Modeling for Robust Speech Recognition.50.422017
Joint Training Of Multi-Channel-Condition Dereverberation And Acoustic Modeling Of Microphone Array Speech For Robust Distant Speech Recognition00.342017
Hierarchical Bayesian combination of plug-in maximum a posteriori decoders in deep neural networks-based speech recognition and speaker adaptation.20.372017
Experimental Study on Extreme Learning Machine Applications for Speech Enhancement.50.442017
Bayesian Unsupervised Batch and Online Speaker Adaptation of Activation Function Parameters in Deep Models for Automatic Speech Recognition.20.362017
A reverberation-time-aware DNN approach leveraging spatial information for microphone array dereverberation.00.342017
Adaptation to New Microphones Using Artificial Neural Networks With Trainable Activation Functions.20.362017
Improving Mispronunciation Detection For Non-Native Learners With Multisource Information And Lstm-Based Deep Models20.382017
Towards a direct Bayesian adaptation framework for deep models.00.342016
Using tone-based extended recognition network to detect non-native Mandarin tone mispronunciations.00.342016
A unified approach to transfer learning of deep neural networks with applications to speaker adaptation in automatic speech recognition.160.602016
Deep learning with maximal figure-of-merit cost to advance multi-label speech attribute detection00.342016
Boosting Universal Speech Attributes Classification With Deep Neural Network For Foreign Accent Characterization10.342015
Rapid Adaptation For Deep Neural Networks Through Multi-Task Learning210.572015
Maximum A Posteriori Adaptation Of Network Parameters In Deep Models80.432015
Attribute based lattice rescoring in spontaneous speech recognition00.342014
Feature space maximum a posteriori linear regression for adaptation of deep neural networks.70.492014
An artificial neural network approach to automatic speech processing.250.872014
Dialect levelling in Finnish: a universal speech attribute approach.10.372014
Hermitian Polynomial for Speaker Adaptation of Connectionist Speech Recognition Systems340.952013
Universal attribute characterization of spoken languages for automatic spoken language recognition160.642013
An experimental study on structural-MAP approaches to implementing very large vocabulary speech recognition systems for real-world tasks00.342013
Exploiting deep neural networks for detection-based speech recognition.330.932013
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