Research Area:  Machine Learning

Abstract:

Artificial Neural Networks (ANNs) were created inspired by the neural networks in the human brain and have been widely applied in speech processing. The application areas of ANN include: Speech recognition, speech emotion recognition, language identification, speech enhancement, and speech separation, amongst others. Likewise, given that speech processing performed by humans involves complex cognitive processes known as auditory attention, there has been a growing amount of papers proposing ANNs supported by deep learning algorithms in conjunction with some mechanism to achieve symmetry with the human attention process. However, while these ANN approaches include attention, there is no categorization of attention integrated into the deep learning algorithms and their relation with human auditory attention. Therefore, we consider it necessary to have a review of the different ANN approaches inspired in attention to show both academic and industry experts the available models for a wide variety of applications. Based on the PRISMA methodology, we present a systematic review of the literature published since 2000, in which deep learning algorithms are applied to diverse problems related to speech processing. In this paper 133 research works are selected and the following aspects are described: (i) Most relevant features, (ii) ways in which attention has been implemented, (iii) their hypothetical relationship with human attention, and (iv) the evaluation metrics used. Additionally, the four publications most related with human attention were analyzed and their strengths and weaknesses were determined.

Keywords:  
Attention-Inspired
Artificial Neural Networks
Speech Processing
Deep Learning
Machine Learning

Author(s) Name:  Noel Zacarias-Morales ,Pablo Pancardo, José Adán Hernández-Nolasco and Matias Garcia-Constantino

Publisher name:  MDPI

DOI:  10.3390/sym13020214

Volume Information:  Volume 13 Issue 2

Paper Link:   https://www.mdpi.com/2073-8994/13/2/214