Authors:
Lucas Debatin
1
;
Aluizio Haendchen Filho
2
and
Rudimar L. S. Dazzi
3
Affiliations:
1
University of Vale do Itajaí (UNIVALI) and University Center of Brusque (UNIFEBE), Brazil
;
2
University Center of Brusque (UNIFEBE), Brazil
;
3
University of Vale do Itajaí (UNIVALI), Brazil
Keyword(s):
Voice Recognition, Offline Recognition, Mobile Devices.
Related
Ontology
Subjects/Areas/Topics:
Accessibility and Usability
;
Adaptive and Adaptable User Interfaces
;
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Natural Language Interfaces to Intelligent Systems
;
Neural Network Software and Applications
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
This paper aims to present the state-of-the-art of speech recognition from a systematic review of the literature. For this, 222 papers from four digital repositories were examined. The research followed a methodology composed of questions of search, expression of search and criteria of inclusion and exclusion. After reading the abstract, introduction and conclusion, nine papers were selected. Based on the analysis of the selected papers, we observed that the research prioritizes the following topics: (i) solutions to reduce the error rate; (ii) neural networks for language models; and (iii) n-gram statistical models. However, no solution was offered to provide offline voice recognition on Android mobile devices. The information obtained is very useful in order to acquire knowledge to be used in the development of offline voice recognition in mobile devices. The techniques provide guidelines for the application of the best neural networks and mechanisms for reducing error rates.