A Review on Emotion Recognition Algorithms using Speech Analysis <s> Mel-Frequency Cepstral Coefficients (MFCC) <s> Recently, increasing attention has been directed to the study of the emotional content of speech signals, and hence, many systems have been proposed to identify the emotional content of a spoken utterance. This paper is a survey of speech emotion classification addressing three important aspects of the design of a speech emotion recognition system. The first one is the choice of suitable features for speech representation. The second issue is the design of an appropriate classification scheme and the third issue is the proper preparation of an emotional speech database for evaluating system performance. Conclusions about the performance and limitations of current speech emotion recognition systems are discussed in the last section of this survey. This section also suggests possible ways of improving speech emotion recognition systems. <s> BIB001 </s> A Review on Emotion Recognition Algorithms using Speech Analysis <s> Mel-Frequency Cepstral Coefficients (MFCC) <s> With the advent of technology, speech recognition is no longer just the capability of the humans. Voice based interfaces can turn most favorable for human computer interaction if computers respond according to its users emotional state. Emotion recognition from speech is a challenging problem as the system has to interact with diverse user utterances. This paper presents an age driven speech emotion recognition system. The performed emotion recognition experiment reveals that, the classifier model trained according to its user's age group show improved accuracy over the model based on simply acoustic features. The age and emotion detection method adopted employs extraction of basic prosodic and spectral feature from the emotional speech corpuses and uses Support Vector Machine (SVM) algorithm for classification. <s> BIB002 </s> A Review on Emotion Recognition Algorithms using Speech Analysis <s> Mel-Frequency Cepstral Coefficients (MFCC) <s> Nowadays, there are many beautiful recitation of Al-Quran available. Quranic recitation has its own characteristics, and the problem to identify the reciter is similar to the speaker recognition/identification problem. The objective of this paper is to develop Quran reciter identification system using Mel-frequency Cepstral Coefficient (MFCC) and Gaussian Mixture Model (GMM). In this paper, a database of five Quranic reciters is developed and used in training and testing phases. We carefully randomized the database from various surah in the Quran so that the proposed system will not prone to the recited verses but only to the reciter. Around 15 Quranic audio samples from 5 reciters were collected and randomized, in which 10 samples were used for training the GMM and 5 samples were used for testing. Results showed that our proposed system has 100% recognition rate for the five reciters tested. Even when tested with unknown samples, the proposed system is able to reject it. <s> BIB003 </s> A Review on Emotion Recognition Algorithms using Speech Analysis <s> Mel-Frequency Cepstral Coefficients (MFCC) <s> Of the many audio features available, this paper focuses on the comparison of two most popular features, i.e. line spectral frequencies (LSF) and Mel-Frequency Cepstral Coefficients. We trained a feedforward neural network with various hidden layers and number of hidden nodes to identify five different languages, i.e. Arabic, Chinese, English, Korean, and Malay. LSF, MFCC, and combination of both features were extracted as the feature vectors. Systematic experiments have been conducted to find the optimum parameters, i.e. sampling frequency, frame size, model order, and structure of neural network. The recognition rate per frame was converted to recognition rate per audio file using majority voting. On average, the recognition rate for LSF, MFCC, and combination of both features are 96%, 92%, and 96%, respectively. Therefore, LSF is the most suitable features to be utilized for language identification using feedforward neural network classifier. <s> BIB004
The Mel-frequency cepstral coefficients (MFCC) is one of the most popular audio feature BIB003 BIB004 . It is a representation of the speech signals where a feature called the cepstrum of a windowed short-time signal is derived from the FFT of that signal. Afterwards the signal goes to the frequency axis of the melfrequency scale using a log based transform, and then decorrelated using a modified Discrete Cosine Transform  . The steps to extract MFCC features, including pre-emphasis, frame blocking and windowing, FFT magnitude, Mel filterbank, log energy, and DCT as explained in BIB004 . MFCC utilizes the mel-scale, which is tuned to the human's ear frequency response. Due to this, MFCC has been proven to be invaluable in the speech recognition field, and has been attempted to be integrated with emotion recognition BIB002 . According to BIB001 , Spectral audio features such as MFCC is best suited for a N-way classifiers.