Tag Archives: UBM

Information Retrieval Publications Speech Processing Video Analysis

UBM-GMM based Text-Independent Speaker Recognition

Speaker recognition systems are often used in the field of security, and a common example of their use is client voice authentication for some secured applications. Another application of speaker recognition is segmentation into homogenous parts of speech where each segment corresponds to one speaker’s speech. This process can also be very useful for improving the accuracy of speech recognition systems. Speech can also be used in the field of audio indexing. Recognizing the identity of speakers in a multi-speaker audio stream can provide some usable knowledge about its content. Two types of speaker recognition system exist: the text-dependent and text-independent systems. The first are speaker recognition systems where the verification texts and those saved during the enrollment phase are the same. As in online video indexing, the sentences are a priori unknown; we will focus here on text-independent systems. This paper is organized as follows: first we present the GMM classifier, and then the principle of LLR (LikeLihood Ratio) detection used to decide on the score given by a tested utterance.

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Audio Analysis Information Retrieval Machine Learning Publications Speech Processing

Gaussian Mixture Model Supervectors

Gaussian Mixture Model (GMM) supervectors (GSV) are generally used in speaker recognition tasks. However, they can be used for the classification of audio events, especially when the training dataset is very limited. This is the case for the recognition of some types of sound, such as “gunshots”, where the variation from one sample to another is small (so the number of various stimuli of these types can be limited). Thus, in a supervised classification, rather than directly using the features vectors as the classifier input, they are transformed into GSV beforehand. This transformation aims at compensating the limitation of the stimuli variability in the training database. In the following, we will present an introduction to Gaussian Mixture Models, the core idea of GSV, and then we will present the GSV concept.

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