Similarity measurement is an important part of speaker identification. This study has modified the similarity measurement technique performed in previous studies. Previous studies used the sum of the smallest distance between the input vectors and the codebook vectors of a particular speaker. In this study, the technique has been modified by selecting a particular speaker codebook which has the...

The transmission of audio data via the Internet Things makes such vulnerable to tampering. Moreover, availability sophisticated tampering tools has allowed mobsters change context by altering their segments. Tampered may result in unpleasant situations for any member society. To avoid circumstances, a new forgery detection system is proposed this study. This can be deployed edge devices identif...

Human recognizes speech emotions by extracting features from the speech signals received through the cochlea and later passed the information for processing. In this paper we propose the use of Mel-Frequency Cepstral Coefficient (MFCC) to extract the speech emotion information to provide both the frequency and time domain information for analysis. Since features extracted using the MFCC simulat...

Heart murmur recognition and classification play an important role in the auscultative diagnosis. The method based on hidden markov model (HMM) was presented to recognize the heart murmur. The murmur was isolated on basis of the principle of wavelet analysis considering the time-frequency characteristics of the heart murmur. This method uses Mel frequency cepstral coefficient (MFCC) to extract ...

Speech babble is one of the most challenging noise interference due to its speaker/speech like characteristics for speech and speaker recognition systems. Performance of such systems strongly degrades in the presence of background noise, like the babble noise. Existing techniques solve this problem by additional processing of speech signal to remove noise. In contrast to existing works, the aim...

This paper presents a novel low complexity, frequency domain algorithm for reconstruction of speech from the melfrequency cepstral coe cients (MFCC), commonly used by speech recognition systems, and the pitch frequency values. The reconstruction technique is based on the sinusoidal speech representation. A set of sine-wave frequencies is derived using the pitch frequency and voicing decisions, ...

This paper explores the benefits of transforming spectral peaks in voice conversion. First, in examining classic GMMbased transformation with cepstral coefficients, we show that the lack of transformed data variance ("over-smoothing") can be related to the choice of spectral parameterization. Consequently, we propose an alternative parameterization using spectral peaks. The peaks are transforme...

We study effects of additive white noise on the cepstral representation of speech signals. Distribution of each individual cepstrum coefficient of speech is shown to depend strongly on noise and to overlap significantly with the cepstrum distribution of noise. Based on these studies, we suggest a scalar quantity, V, equal to the sum of weighted cepstral coefficients, which is able to classify f...

In this paper a method of text-independent speaker recognition using discrete vector quantization is presented. The identification experiments were performed in a closed set of 599 speakers and two various types of features were tested: cepstral mean subtraction coefficients and mel-frequency cepstral coefficients. The effect of the various codebook size on the speaker identification performanc...

We have shown previously that vocal tract normalization (VTN) results in a linear transformation in the cepstral domain. In this paper we show that Mel-frequency warping can equally well be integrated into the framework of VTN as linear transformation on the cepstrum. We show examples of transformation matrices to obtain VTN warped Mel-frequency cepstral coefficients (VTN-MFCC) as linear transf...