The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
A human speaker recognition expert often observes the speech spectrogram in multiple different scales for speaker recognition, especially under the short utterance condition. Inspired by this action, this paper proposes a novel multi-resolution time frequency feature (MRTF) extraction method, which is obtained by performing a 2-Dimensional discrete cosine transform (DCT) in multi-scale on the time...
Short utterance is a great challenge for speaker recognition, for there is very limited data can be used for training and testing. To give a robust estimation, the amount of model parameters for the short utterance should be less than that for the long utterance; however, this may impede the models descriptive capability. In this paper, we propose a multi-scale kernel (MSK) approach to solve this...
At present, i-vector model has become the state-of-the-art technology for speaker recognition. It represents speech utterance to a low-dimensional fix-length compact i-vector. For some real application, i-vector extraction procedure is relatively slow and requires too much memories. Some numerical approximation based fast extraction methods have been proposed to speed up the computation and to save...
Combination of different features has been proved to be a good method for improving performance in speech recognition. In speaker recognition (SRE), various features have also been developed to reflect complementary aspects of speaker's characteristics. This paper proposed an effective multi-feature combination in speaker recognition. In order to avoid the “dimensionality disaster” and to delimit...
One of the most difficult challenges for speaker recognition is dealing with channel variability. In this paper, several new techniques of cross-channel compensation of speech signal are used for text-independent speaker verification system. These new techniques include wideband noise reduction, echo cancellation, a simplified feature-domain latent factor analysis and data-driven score normalization...
This paper realizes a text-independent, speaker verification system on a system on chip (SOC) platform. The system uses Mel-frequency cepstral coefficients (MFCC) features with a Gaussian mixture model-universal background model (GMM-UBM) speaker model. To deal with resource limitations, a new speaker-centric score normalization technique is introduced. This normalization technique results in a relative...
Channel variability is the major cause of performance degradation in text-independent speaker verification. Compensation technology in feature, model or score domain has been widely applied to baseline systems to mitigate mismatch. Newly proposed Gaussian mixture models super vector-support vector machine (GMM-SVM or GSV-SVM) baseline system has proven successful through integrating advantages of...
In this paper, we propose a speaker segmentation algorithm using confidence measures, named CM-DISTBIC, which inserts a confidence score computation and fusion procedure into the two-step DISTBIC and MDISTBIC. In the first step, symmetric Kullback-Leibler distance (KL2) distance is replaced by Bayesian information criterion (BIC) distance to obtain a lower misdetection rate. In the second step, three...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.