Acoustic detection and classification of microchiroptera using machine learning: Lessons learned from automatic speech recognition

2006 ◽  
Vol 119 (3) ◽  
pp. 1817-1833 ◽  
Author(s):  
Mark D. Skowronski ◽  
John G. Harris
Keyword(s):  
Machine Learning ◽  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Lessons Learned ◽  
Acoustic Detection

Machine Learning in Automatic Speech Recognition: A Survey

2015 ◽  
Vol 32 (4) ◽  
pp. 240-251 ◽  
Author(s):  
Jayashree Padmanabhan ◽  
Melvin Jose Johnson Premkumar
Keyword(s):  
Machine Learning ◽  
Speech Recognition ◽  
Automatic Speech Recognition

A Life-Based Classifier for Automatic Speech Recognition

2014 ◽  
Vol 679 ◽  
pp. 189-193 ◽  
Author(s):  
Rosemary T. Salaja ◽  
Ronan Flynn ◽  
Michael Russell
Keyword(s):  
Pattern Recognition ◽  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Artificial Life ◽  
Recognition System ◽  
Recognition Problem ◽  
Speech Recognition System ◽  
Pattern Recognition Problem ◽  
Asr System

Research in speech recognition has produced different approaches that have been used for the classification of speech utterances in the back-end of an automatic speech recognition (ASR) system. As speech recognition is a pattern recognition problem, classification is an important part of any speech recognition system. This paper proposes a new back-end classifier that is based on artificial life (ALife) and describes how the proposed classifier can be used in a speech recognition system.

scholarly journals Javanese Gender Speech Recognition Based on Machine Learning Using Random Forest and Neural Network

SISFORMA ◽  
2020 ◽  
Vol 6 (2) ◽  
pp. 50
Author(s):  
Kristiawan Nugroho
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Speech Recognition ◽  
Random Forest ◽  
Accuracy Rate ◽  
Word Error Rate ◽  
Human Voice ◽  
Preprocessing Technique ◽  
The Right

Speech is a means of communication between people throughout the world. At present research in the field of speech recognition continues to develop in producing a robust method in various research variants. However decreasing the word error rate or reducing noise is still a problem that is still being investigated until now. The purpose of this study is to find the right method with high accuracy to classify the gender voices of Javanese. This research used a human voice dataset of both men and women from the Javanese tribe which was recorded and then processed using a noise reduction preprocessing technique with the MFCC extraction feature method and then classified using 2 machine learning methods, namely Random Forest and Neural Network. Evaluation results indicate that the classification of Javanese accent speech accents results in an accuracy rate of 91.3 % using Random Forest and 92.2% using Neural Network.

scholarly journals Automatic Speech Recognition (ASR) Systems for Learning Arabic Language and Al-Quran Recitation: A Review

2021 ◽  
Vol 10 (7) ◽  
pp. 91-100
Author(s):  
Nazik O’mar Balula ◽  
Mohsen Rashwan ◽  
Shrief Abdou
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Speech Recognition ◽  
Language Processing ◽  
Automatic Speech Recognition ◽  
Communication Technologies ◽  
Arabic Language ◽  
Literature Survey ◽  
Verification Systems

This paper provides a literature survey about Automatic Speech Recognition (ASR) systems for learning Arabic language and Al-Quran Recitation. The growth in communication technologies and AI (specially Machine learning and Deep learning) led researchers in ASR field to thinking of and developing ASR systems which mimic humans in their understand of natural speech and recognition. One of the most important applications in ASR is natural language processing (NLP). Arabic language is one of these languages. ASR systems which developed for Arabic language help Arabs and non-Arabs in learning Arabic language and so Al-Quran recitation and memorization in proper way according to recitation rules (Tajweed). This paper concentrate on ASR systems in general, challenges, PROS, CONS, Arabic language ASR systems and challenges faced them and finally Al-Quran recitation verification systems.

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