Speaker Independent Emotion Recognition Using Functional Link Network Classifier Firoz Shah.A and Babu Anto.P School of Information Science and Technology, Kannur University
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Abstract: This paper deals with a novel approach towards detecting emotions from Malayalam speech. We used Discrete Wavelet Transforms (DWT) for feature extraction and Functional Link Network (FLN) Classifier for recognizing different emotions. From this experiment, the machine can recognize four different emotions such as neutral, happy, sad and anger with an overall recognition accuracy of 63.75%.
Keywords: Emotion Recognition, Discrete Transforms, Functional Link Network Classifier.
Wavelet
I. INTRODUCTION Speech is the most effective communication tool among human beings. Speech signals with emotional contents make the communication more effective. The significance and effectiveness of emotions in human interactions is one of the interesting areas in speech based studies. Emotions handle a prominent role in human decision making. Automatic Emotion Recognition (AER) from speech is an important area of research for the last two decades [1]. Automatic Emotion Recognition from speech simply means to find out the exact emotional content in a spoken dialogue, and make a machine, able to recognize the exact emotional state of the speaker. To find out the exact emotional contents from speech, we have to extract certain parameters that characterize different emotions. [2]. Because a good feature can only give a good analysis and recognition results. Emotion recognition systems find applications in intelligent systems, lie detections, robotics, security applications and spoken dialogue systems. II. FEATURE EXTRACTION The feature extraction really means to convert the speech signal to some parametric representations. Since the speech signal is time –varying itself, and so it is necessary to need some parametric representations to analyze the signal thoroughly. We can analyze a speech signal in time, frequency and spectral domains. It is very difficult to extract reliable features from speech signals because of its quasi periodic nature of the speech signals. For the parametric representation of this work we have used the wavelet transform technique.
A. Wavelet Transforms Wavelet transform is the only linear transformation mechanism can be used for non-stationary signals at varying resolutions by decomposing the signal into their frequency bands. Wavelet analysis finds out the correlation between the signal to be analyzed and a wavelet function Ψ (t). The similarity between the input signal and the function Ψ (t) computed separately for different time intervals resulting in two dimensional representations by using an analyzing function or mother wavelet [3]. The analyzing function Ψ (t) can be taken into account as a wavelet if it satisfies the following conditions. 1. The Wavelet must have finite energy E=_∞ ∫- ∞ │ Ψ (t) │2 dt
