Speech Representation Learning Using Unsupervised Data-Driven Modulation Filtering for Robust ASR

2017 ◽  
Author(s):  
Purvi Agrawal ◽  
Sriram Ganapathy
Keyword(s):  
Representation Learning ◽  
Data Driven ◽  
Speech Representation ◽  
Modulation Filtering

scholarly journals An Exploratory Study of COVID-19 Information on Twitter in the Greater Region

2021 ◽  
Vol 5 (1) ◽  
pp. 5
Author(s):  
Ninghan Chen ◽  
Zhiqiang Zhong ◽  
Jun Pang
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Online Social Networks ◽  
Exploratory Study ◽  
Representation Learning ◽  
Data Driven ◽  
Learning Methods ◽  
Twitter Users ◽  
Over Time

The outbreak of the COVID-19 led to a burst of information in major online social networks (OSNs). Facing this constantly changing situation, OSNs have become an essential platform for people expressing opinions and seeking up-to-the-minute information. Thus, discussions on OSNs may become a reflection of reality. This paper aims to figure out how Twitter users in the Greater Region (GR) and related countries react differently over time through conducting a data-driven exploratory study of COVID-19 information using machine learning and representation learning methods. We find that tweet volume and COVID-19 cases in GR and related countries are correlated, but this correlation only exists in a particular period of the pandemic. Moreover, we plot the changing of topics in each country and region from 22 January 2020 to 5 June 2020, figuring out the main differences between GR and related countries.

scholarly journals Sampling Strategies in Siamese Networks for Unsupervised Speech Representation Learning

2018 ◽  
Author(s):  
Rachid Riad ◽  
Corentin Dancette ◽  
Julien Karadayi ◽  
Neil Zeghidour ◽  
Thomas Schatz ◽  
...  
Keyword(s):  
Representation Learning ◽  
Sampling Strategies ◽  
Speech Representation ◽  
Siamese Networks

scholarly journals wav2vec-C: A Self-Supervised Model for Speech Representation Learning

2021 ◽  
Author(s):  
Samik Sadhu ◽  
Di He ◽  
Che-Wei Huang ◽  
Sri Harish Mallidi ◽  
Minhua Wu ◽  
...  
Keyword(s):  
Representation Learning ◽  
Speech Representation

scholarly journals Survey of Deep Representation Learning for Speech Emotion Recognition

2021 ◽  
Author(s):  
Siddique Latif ◽  
Rajib Rana ◽  
Sara Khalifa ◽  
Raja Jurdak ◽  
Junaid Qadir ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
General Setting ◽  
Representation Learning ◽  
Data Driven ◽  
Speech Emotion Recognition ◽  
Feature Engineering ◽  
Acoustic Features ◽  
Learning Techniques ◽  
Comprehensive Survey ◽  
Hierarchical Representations

<div>Traditionally, speech emotion recognition (SER) research has relied on manually handcrafted acoustic features using feature engineering. However, the design of handcrafted features for complex SER tasks requires significant manual effort, which impedes generalisability and slows the pace of innovation. This has motivated the adoption of representation learning techniques that can automatically learn an intermediate representation of the input signal without any manual feature engineering. Representation learning has led to improved SER performance and enabled rapid innovation. Its effectiveness has further increased with advances in deep learning (DL), which has facilitated deep representation learning where hierarchical representations are automatically learned in a data-driven manner. This paper presents the first comprehensive survey on the important topic of deep representation learning for SER. We highlight various techniques, related challenges and identify important future areas of research. Our survey bridges the gap in the literature since existing surveys either focus on SER with hand-engineered features or representation learning in the general setting without focusing on SER.</div>

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