This paper focuses on providing better performance in speaker identification technique by integrating FIR1 filter with audio recognition techniques. Main objective is to recognize speakers with wave transformation to attain improved outcomes. Speaker identification is becoming popular in real time security systems to enhance the person recognition i.e. it can find unauthorized persons easily. T...

This paper presents a memory efficient single pass speech recognizer that makes use of pre-computed FMLLR transformations for online speaker adaptation. For that purpose we apply unsupervised segment clustering to the training corpus, create a transformation matrix for each cluster, and train a text-independentGaussian mixture classifier for cluster selection during runtime. We use the RWTH Aac...

One of the largest challenges in speaker recognition is dealing with speaker-emotion variability problem. Nowadays, compensation techniques are the main solutions to this problem. In these methods, all kinds of speakers’ emotion speech should be elicited thus it is not user-friendly in the application. Therefore the basic problem is how to get the distribution of speakers’ emotion speech and ho...

Frequency warping approaches to speaker normalization have been proposed and evaluated on various speech recognition tasks [1, 2, 3]. These techniques have been found to signi cantly improve performance even for speaker independent recognition from short utterances over the telephone network. In maximum likelihood (ML) based model adaptation a linear transformation is estimated and applied to t...

A method of speaker adaptation for continuous density hidden Markov models (HMMs) is presented. An initial speaker-independent system is adapted to improve the modelling of a new speaker by updating the HMM parameters. Statistics are gathered from the available adaptation data and used to calculate a linear regressionbased transformation for the mean vectors. The transformation matrices are cal...

This paper presents a new approach on transforming a modal voice into a rough or growl voice. The goal of such transformations is to be able to enhance voice expressiveness in singing voice productions. Both techniques work with spectral models and are based on adding sub-harmonics in frequency domain to the original input voice spectrum.

speaker verification from talking a few words of sentences has many applications. many methods as dtw, hmm, vq and mq can be used for speaker verification. we applied mq for its precise, reliable and robust performance with computational simplicity. we also used pitch frequency and log gain contour for further improvement of the system performance.

Speaker adaptation and speaker change detection have both been studied extensively to improve automatic speech recognition (ASR). In many cases, these two problems are investigated separately: is implemented first obtain single-speaker regions, then performed using the derived segments for improved ASR. However, in an online setting, we want achieve goals a single pass. this study, propose neur...

In state-of-the-art speaker recognition system, universal background model (UBM) plays a role of acoustic space division. Each Gaussian mixture of trained UBM represents one distinct acoustic region. The posterior probabilities of features belonging to each region are further used as core components of Baum-Welch statistics. Therefore, the quality of estimated Baum-Welch statistics depends high...

In telephone-based speaker verification, the channel conditions can be varied significantly from sessions to sessions. Therefore, it is desirable to estimate the channel conditions online and compensate the acoustic distortion without prior knowledge of the channel characteristics. Because no a priori knowledge is used, the estimation accuracy depends greatly on the length of the verification u...