This project's 'HMM Based Automatic Speech Recognition Analysis main motive is just to generate an Automatic speech recognition which is clear an accurate using Hidden Markov Model (HMM) to get accurate results at number of frequency ranges related to human voice. Here is a record of 12 different words which is recorded by using a number of different speakers that includes male and female both ...

This paper describes a hybrid technique for speaker recognition. Speaker recognition is that the method of identifying the person based on characteristics like pitch, tone, Cepstral coefficients in the speech wave. Here DWT and MFCC technique is employed for feature extraction. A mix of two or lot of techniques is named hybrid technique. DWT means divide the speech signal completely into differ...

Two methods of spectral analysis for noisy speech recognition are proposed and tested in a speaker independent word recognition experiment under an additive white Gaussian noise environment. One is Mel-frequency cepstral coefficients (MFCC) spectral analysis on the autocorrelation sequence of the speech signal and the other is MFCC spectral analysis on its double autocorrelation sequence. The w...

Conventional automatic speaker verification systems are based on cepstral features like Mel-scale Frequency Cepstrum Coefficient (MFCC), or Linear Predictive Cepstrum Coefficient (LPCC). Recent published works showed that the use of complementary features can significantly improve the system performances. In this paper, we propose to use an evolution strategy to optimize the complementarity of ...

Automatic Speech Recognition (ASR) technology is a way to interface with computer. In this paper we describe speech recognition technique using multiple codebooks of MFCC derived features. The proposed algorithm is useful in detecting isolated words of speech. In this algorithm we first create database i.e. codebook by calculating mel frequency cepstral coefficient first and then codeword for e...

This paper proposes a method of predicting the formant frequencies of a frame of speech from its mel-frequency cepstral coefficient (MFCC) representation. Prediction is achieved through the creation of a Gaussian mixture model (GMM) which models the joint density of formant frequencies and MFCCs. Using this GMM and an input MFCC vector, a maximum a posteriori (MAP) prediction of the formant fre...