Abstract--The main objective of this research is to develop a speech emotion recognition system using residual phase and MFCC features with autoassociative neural network (AANN). The speech emotion recognition system classifies the speech emotion into predefined categories such as anger, fear, happy, neutral or sad. The proposed technique for speech emotion recognition (SER) has two phases : Fe...

In this paper, improvement of an ASR system for Hindi language, based on Vector quantized MFCC as feature vectors and HMM as classifier, is discussed. MFCC features are usually pre-processed before being used for recognition. One of these pre-processing is to create delta and delta-delta coefficients and append them to MFCC to create feature vector. This paper focuses on all digits in Hindi (Ze...

A state of the art Speaker Identification (SI) system requires a robust feature extraction unit followed by a speaker modeling scheme for generalized representation of these features. Over the years, Mel-Frequency Cepstral Coefficients (MFCC) modeled on the human auditory system has been used as a standard acoustic feature set for speech related applications. On a recent contribution by authors...

This paper presents a new feature extraction algorithm called PNCC that is based on auditory. Major new features of PNCC processing include the use of a power-law nonlinearity that replaces the traditional log nonlinearity used in MFCC coefficients, and a novel algorithm to suppress background excitation using medium-duration power estimation based on the ratio of the arithmetic mean to the geo...

The most popular speech feature extractor used in automatic speech recognition (ASR) systems today is the mel frequency cepstral coefficient (mfcc) algorithm. Introduced in 1980, the filter bank-based algorithm eventually replaced linear prediction cepstral coefficients (lpcc) as the premier front end, primarily because of mfcc’s superior robustness to additive noise. However, mfcc does not app...

Gabor features have been proposed for extracting spectro-temporal modulation information, and yielding significant improvements in recognition performance. In this paper, we propose the integration of Gabor posteriors with MFCC posteriors, yielding a relative improvement of 14.3% over an MFCC Tandem system. We analyze for different types of acoustic units the complementarity between Gabor featu...

ICA which is generally used for blind source separation problem has been tested for feature extraction in Speech recognition system to replace the phoneme based approach of MFCC. Applying the Cepstral coefficients generated to ICA as preprocessing has developed a new signal processing approach. This gives much better results against MFCC and ICA separately, both for word and speaker recognition...