In speaker recognition, it is a problem that speech f e a-ture varies depending on sentences and time diierence. This variation is mainly attributed to the variation of phonetic information and speaker information included in speech data. If these two kinds of information are separated each other, robust speaker recognition will be realized. In this study, w e propose a speaker identiica-tion a...

On-line Speaker indexing is useful for multimedia applications such as meeting or teleconference archiving and browsing. It sequentially detects the points where a speaker identity changes in a multi-speaker audio stream, and classifies each speaker segment. The main problem of on-line processing is that we can use only current and previous information in the data stream for any decisioning. To...

This paper presents a new method for text independent speaker recognition. We embed both training and test sessions into a session space. The session space is a direct sum of a common-speaker subspace and a speaker-unique subspace. The common-speaker subspace is Euclidean and is spanned by a set of reference sessions. Kernel-PCA is used to explicitly embed sessions into the common-speaker subsp...

Developing a good speaker embedding has received tremendous interest in the speech community. Speaker representations such as i-vector, d-vector have shown their superiority in speaker recognition, speaker adaptation and other related tasks. However, not much is known about which properties are exactly encoded in these speaker embeddings. In this work, we make an in-depth investigation on three...

In this paper we describe the speaker tracking and clustering system that we implemented for the ESTER evaluation campaign. We present some experiments on normalization in speaker tracking, in particular concerning the use of t-norm for speaker tracking in broadcast news. Results show that the use of t-norm significantly improves the performance at low false alarm rates. In a second part of the...

Speaker indexing sequentially detects points where speaker identity changes in a multi-speaker audio stream, and classifies each detected segment according to the speaker’s identity. In unsupervised speaker indexing scenarios, there is no prior information/data about the speakers in the target data. To address this issue, a predetermined generic “speaker-independent” model set, called Sample Sp...

In this paper, a novel Convolutional Neural Network architecture has been developed for speaker verification in order to simultaneously capture and discard speaker and non-speaker information, respectively. In training phase, the network is trained to distinguish between different speaker identities for creating the background model. One of the crucial parts is to create the speaker models. Mos...