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All Journal Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics
Mimi Aminah binti Wan Nordin
International Islamic University Malaysia
Computer Science & IT Electrical & Electronics Engineering Engineering

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On the use of voice activity detection in speech emotion recognition Muhammad Fahreza Alghifari; Teddy Surya Gunawan; Mimi Aminah binti Wan Nordin; Syed Asif Ahmad Qadri; Mira Kartiwi; Zuriati Janin
Bulletin of Electrical Engineering and Informatics Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (903.469 KB) | DOI: 10.11591/eei.v8i4.1646

Abstract

Emotion recognition through speech has many potential applications, however the challenge comes from achieving a high emotion recognition while using limited resources or interference such as noise. In this paper we have explored the possibility of improving speech emotion recognition by utilizing the voice activity detection (VAD) concept. The emotional voice data from the Berlin Emotion Database (EMO-DB) and a custom-made database LQ Audio Dataset are firstly preprocessed by VAD before feature extraction. The features are then passed to the deep neural network for classification. In this paper, we have chosen MFCC to be the sole determinant feature. From the results obtained using VAD and without, we have found that the VAD improved the recognition rate of 5 emotions (happy, angry, sad, fear, and neutral) by 3.7% when recognizing clean signals, while the effect of using VAD when training a network with both clean and noisy signals improved our previous results by 50%.
The disruptometer: an artificial intelligence algorithm for market insights Mimi Aminah binti Wan Nordin; Dmitry Vedenyapin; Muhammad Fahreza Alghifari; Teddy Surya Gunawan
Bulletin of Electrical Engineering and Informatics Vol 8, No 2: June 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (550.211 KB) | DOI: 10.11591/eei.v8i2.1494

Abstract

Social media data mining is rapidly developing to be a mainstream tool for marketing insights in today’s world, due to the abundance of data and often freely accessed information. In this paper, we propose a framework for market research purposes called the Disruptometer. The algorithm uses keywords to provide different types of market insights from data crawling. The preliminary algorithm data-mines information from Twitter and outputs 2 parameters-Product-to-Market Fit and Disruption Quotient, which is obtained from a brand’s customer value proposition, problem space, and incumbent space. The algorithm has been tested with a venture capitalist portfolio company and market research firm to show high correlated results. Out of 4 brand use cases, 3 obtained identical results with the analysts ‘studies.
The disruptometer: an artificial intelligence algorithm for market insights Mimi Aminah binti Wan Nordin; Dmitry Vedenyapin; Muhammad Fahreza Alghifari; Teddy Surya Gunawan
Bulletin of Electrical Engineering and Informatics Vol 8, No 2: June 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (550.211 KB) | DOI: 10.11591/eei.v8i2.1494

Abstract

Social media data mining is rapidly developing to be a mainstream tool for marketing insights in today’s world, due to the abundance of data and often freely accessed information. In this paper, we propose a framework for market research purposes called the Disruptometer. The algorithm uses keywords to provide different types of market insights from data crawling. The preliminary algorithm data-mines information from Twitter and outputs 2 parameters-Product-to-Market Fit and Disruption Quotient, which is obtained from a brand’s customer value proposition, problem space, and incumbent space. The algorithm has been tested with a venture capitalist portfolio company and market research firm to show high correlated results. Out of 4 brand use cases, 3 obtained identical results with the analysts ‘studies.
On the use of voice activity detection in speech emotion recognition Muhammad Fahreza Alghifari; Teddy Surya Gunawan; Mimi Aminah binti Wan Nordin; Syed Asif Ahmad Qadri; Mira Kartiwi; Zuriati Janin
Bulletin of Electrical Engineering and Informatics Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (903.469 KB) | DOI: 10.11591/eei.v8i4.1646

Abstract

Emotion recognition through speech has many potential applications, however the challenge comes from achieving a high emotion recognition while using limited resources or interference such as noise. In this paper we have explored the possibility of improving speech emotion recognition by utilizing the voice activity detection (VAD) concept. The emotional voice data from the Berlin Emotion Database (EMO-DB) and a custom-made database LQ Audio Dataset are firstly preprocessed by VAD before feature extraction. The features are then passed to the deep neural network for classification. In this paper, we have chosen MFCC to be the sole determinant feature. From the results obtained using VAD and without, we have found that the VAD improved the recognition rate of 5 emotions (happy, angry, sad, fear, and neutral) by 3.7% when recognizing clean signals, while the effect of using VAD when training a network with both clean and noisy signals improved our previous results by 50%.
The disruptometer: an artificial intelligence algorithm for market insights Mimi Aminah binti Wan Nordin; Dmitry Vedenyapin; Muhammad Fahreza Alghifari; Teddy Surya Gunawan
Bulletin of Electrical Engineering and Informatics Vol 8, No 2: June 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (550.211 KB) | DOI: 10.11591/eei.v8i2.1494

Abstract

Social media data mining is rapidly developing to be a mainstream tool for marketing insights in today’s world, due to the abundance of data and often freely accessed information. In this paper, we propose a framework for market research purposes called the Disruptometer. The algorithm uses keywords to provide different types of market insights from data crawling. The preliminary algorithm data-mines information from Twitter and outputs 2 parameters-Product-to-Market Fit and Disruption Quotient, which is obtained from a brand’s customer value proposition, problem space, and incumbent space. The algorithm has been tested with a venture capitalist portfolio company and market research firm to show high correlated results. Out of 4 brand use cases, 3 obtained identical results with the analysts ‘studies.
On the use of voice activity detection in speech emotion recognition Muhammad Fahreza Alghifari; Teddy Surya Gunawan; Mimi Aminah binti Wan Nordin; Syed Asif Ahmad Qadri; Mira Kartiwi; Zuriati Janin
Bulletin of Electrical Engineering and Informatics Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (903.469 KB) | DOI: 10.11591/eei.v8i4.1646

Abstract

Emotion recognition through speech has many potential applications, however the challenge comes from achieving a high emotion recognition while using limited resources or interference such as noise. In this paper we have explored the possibility of improving speech emotion recognition by utilizing the voice activity detection (VAD) concept. The emotional voice data from the Berlin Emotion Database (EMO-DB) and a custom-made database LQ Audio Dataset are firstly preprocessed by VAD before feature extraction. The features are then passed to the deep neural network for classification. In this paper, we have chosen MFCC to be the sole determinant feature. From the results obtained using VAD and without, we have found that the VAD improved the recognition rate of 5 emotions (happy, angry, sad, fear, and neutral) by 3.7% when recognizing clean signals, while the effect of using VAD when training a network with both clean and noisy signals improved our previous results by 50%.
Co-Authors Dmitry Vedenyapin Mira Kartiwi Muhammad Fahreza Alghifari Syed Asif Ahmad Qadri Teddy Surya Gunawan Zuriati Janin