A Review on Emotion Recognition Algorithms using Speech Analysis <s> Linear Predictive Coding Coefficients (LPCC) <s> Automatic speech recognition in different languages, spoken in different areas of any country, is one of the major research area in the field of signal processing. This paper presents an improved MFCC algorithm for Bundelkhandi digit speech recognition. Here speech digit features are extracted by using modified Mel Frequency Cepstral Coefficient algorithm (MFCC). In this modified MFCC algorithm, one additional filter, named as 1-D median filter, is used. Now this modified MFCC algorithm is applied on Bundelkhandi digits dataset to gather the speech features. Similarly 1-D Median filter is used with LPC algorithm to capture the Bundelkhandi digits dataset. Finally speech features, which are collected from modified MFCC and modified LPC algorithm, are combined and used as an input to back propagation neural network for recognizing particular digits. Experiments are performed on Bundelkhandi digit dataset (0 to 9), made with the help of sound recorder. These results show that proposed algorithm performance is better than simple MFCC and LPC algorithm. <s> BIB001 </s> A Review on Emotion Recognition Algorithms using Speech Analysis <s> Linear Predictive Coding Coefficients (LPCC) <s> Good features are critical for the research of speech emotion recognition. This paper based on the theory of deep learning, and phonetic features were extracted by using the method of deep auto-encoder (DAE). In this paper, a deep auto-encoder containing five hidden layers was designed. To get the input data, we divided the audio into short frames, each frame of speech emotion signal was then decomposed with wavelet, and was calculated the Fourier transform. Higher features were learned with deep automatic encoder, and some traditional features such as MFCC, LPCC were also extracted. With high-level features and traditional features, support vector machine was used for classification and recognition. Compared with the traditional features, the results show that the highest recognition accuracy rate can be reached 86.41%. <s> BIB002
Linear predictive coding (LPC) is a digital method for encoding an analog signal BIB001 . The way LPC works is that it predicts the next value of a signal based on the information it has received in the past, forming a linear pattern. The main objective of LPC to obtain a set of predictor coefficients that will minimize the mean squared error, . The formula used to obtain the LPC coefficients is: where is a frame of the speech signal and p the order of the LPC analysis. LPC encoding generally gives satisfactory quality speech at a lower bit rate and supplies pinpoint approximations of speech parameters. Although LPCC can be considered one of the more traditional features of speech, LPC has contribute to the overall recognition of emotion. In BIB002 , they used LPCC as one of their features and achieved 86.41% recognition.