Proceedings ArticleDOI
Mel-frequency cepstral coefficient analysis in speech recognition
Chin Kim On,Paulraj Murugesa Pandiyan,S. Yaacob,Azali Saudi +3 more
- pp 1-5
TLDR
The zero crossing extraction and the energy level detection are applied to the recorded speech signal for voiced/unvoiced area detection and the extracted MFCC data are further used as inputs for neural network training.Abstract:
Speech recognition is a major topic in speech signal processing. Speech recognition is considered as one of the most popular and reliable biometric technologies used in automatic personal identification systems. Speech recognition systems are used for variety of applications such as multimedia browsing tool, access centre, security and finance. It allows people work in active environment to use computer. For a reliable and high accuracy of speech recognition, simple and efficient representation methods are required. In this paper, the zero crossing extraction and the energy level detection are applied to the recorded speech signal for voiced/unvoiced area detection. The detected voiced signals are applied for segmentation. Further, the MFCC method is applied to all of the segmented windows. The extracted MFCC data are further used as inputs for neural network training.read more
Citations
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Journal Article
Speech and audio signal processing: processing and perception of speech and music [Book Review]
Journal ArticleDOI
A survey of emotion recognition methods with emphasis on E-Learning environments
Maryam Imani,Gholam Ali Montazer +1 more
TL;DR: According to the findings of this research, the multi-modal emotion recognition systems through information fusion as facial expressions, body gestures and user's messages provide better efficiency than the single- modal ones.
Journal ArticleDOI
A novel Adaptive Fractional Deep Belief Networks for speaker emotion recognition
TL;DR: The proposed AFDBN method is used to find out the optimal weights which are used to recognize the emotion efficiently and attains 99.17% accuracy for Berlin database and 97.74% for Telugu database.
Journal ArticleDOI
A Novel Approach for Classification of Speech Emotions Based on Deep and Acoustic Features
TL;DR: In this article, a hybrid architecture based on acoustic and deep features was proposed to increase the classification accuracy in the problem of speech emotion recognition, which consists of feature extraction, feature selection and classification stages.
Journal ArticleDOI
FDBN: Design and development of Fractional Deep Belief Networks for speaker emotion recognition
TL;DR: A new classifier is developed by combining deep belief network (DBN) and Fractional Calculus, trained with the multiple features such as tonal power ratio, spectral flux, pitch chroma and Mel frequency cepstral coefficients (MFCC) to make the emotional classes more separable through the spectral characteristics.
References
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Book
Discrete-Time Speech Signal Processing: Principles and Practice
TL;DR: This chapter discusses the Discrete-Time Speech Signal Processing Framework, a model based on the FBS Method, and its applications in Speech Communication Pathway and Homomorphic Signal Processing.
Journal ArticleDOI
Introduction to artificial neural networks.
Enzo Grossi,Massimo Buscema +1 more
TL;DR: The coupling of computer science and theoretical bases such as nonlinear dynamics and chaos theory allows the creation of 'intelligent' agents, such as artificial neural networks, able to adapt themselves dynamically to problems of high complexity.
Book
Speech and Audio Signal Processing: Processing and Perception of Speech and Music
TL;DR: This Second Edition of Speech and Audio Signal Processing will update and revise the original book to augment it with new material describing both the enabling technologies of digital music distribution and a range of exciting new research areas in automatic music content processing that have emerged in the past five years, driven by the digital music revolution.
Book ChapterDOI
Introduction to Artificial Neural Network
TL;DR: Instead of performing a program consisting of instructions sequentially as in a von Neumann computer, artificial neural nets have their structures in dense interconnection of simple computational elements— the artificial neurons or simply “neurons”, and operate the massive computational elements in parallel to achieve high performance speed.
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