Speaker recognition techniques have traditionally relied on purely acoustic features and models. During the last few years, however, the field of speaker recognition has started to show interest in the use of higher level features. In particular, phonetic decodings modeled with statistical language models (n-grams) have already shown its effectiveness in several research works. However, the rel...

Human Voice is characteristic for an individual. The ability to recognize the speaker by his/her voice can be a valuable biometric tool with enormous commercial as well as academic potential. Commercially, it can be utilized for ensuring secure access to any system. Academically, it can shed light on the speech processing abilities of the brain as well as speech mechanism. In fact, this feature...

This paper presents the data selection method for speaker recognition. Since there is no promise that more data guarantee better results, the way of data selection becomes important. In the GMM-UBM speaker recognition, the UBM is trained to represent the speaker-independent distribution of acoustic features while the GMM speaker model is tailored for a specific speaker. In this study of data se...

Automatic Speaker Recognition systems have been largely dominated by acoustic-spectral based systems, relying in proper modelling of the short-term vocal tract of speakers. However, there is scientific and intuitive evidence that speaker specific information is embedded in the speech signal in multiple shortand long-term characteristics. In this work, a multilevel speaker recognition system com...

The isolated word speech recognition system based on dynamic time warping (DTW) has been developed. Speaker adaptation is performed using speaker recognition techniques. Vector quantization is used to create reference templates for speaker recognition. Linear predictive coding (LPC) parameters are used as features for recognition. Performance is evaluated using 12 words of Lithuanian language p...

We present a new approach to speaker dependent template generation which uses dramatically less storage to represent a speaker's words, with minimal degradation in recognition accuracy. In this approach, the symbolic string produced by a speaker-independent phonetic recognizer is used to represent utterances. We investigate eeective procedures for template generation, and compare the results of...

Voiceprint Recognition System also known as a Speaker Recognition System (SRS) is the best-known commercialized forms of voice Biometrics. Automated speaker recognition is the computing task of validating a user's claimed identity using characteristics extracted from their voices. In contrast to other biometric technologies which are mostly image based and require expensive proprietary hardware...

Speaker-independent system is desirable in many applications where speaker-specific data do not exist. However, if speakerdependent data are available, the system could be adapted to the specific speaker such that the error rate could be significantly reduced. In this paper, DARPA Resource Management task is used as the domain to investigate the performance of speaker-adaptive speech recognitio...

-The significant progress in speaker recognition is by the use of pitch parameters. The accuracy of speaker recognition to a known degree of confidence for forensic tasks is given by the pitch. The experiments performed discriminates an individual and gives the model of a speaker. The analysis is performed on the TIMIT database and the results are 87% effective and are verified by using PRAAT T...

This paper presents the ALIZE/SpkDet open source software packages for text independent speaker recognition. This software is based on the well-known UBM/GMM approach. It includes also the latest speaker recognition developments such as Latent Factor Analysis (LFA) and unsupervised adaptation. Discriminant classifiers such as SVM supervectors are also provided, linked with the Nuisance Attribut...