Infinite models for speaker clustering

Speaker Independent Speech Recognition Using Hidden Markov Models for Persian Isolated Words

Speaker Independent Speech Recognition Using Hidden Markov Models for Persian Isolated Words

A sampling-based speaker clustering using utterance-oriented Dirichlet process mixture model and its evaluation on large scale data

An infinite mixture model is applied to model-based speaker clustering with sampling-based optimization to make it possible to estimate the number of speakers. For this purpose, a framework of non-parametric Bayesian modeling is implemented with the Markov chain Monte Carlo and incorporated in the utterance-oriented speaker model. The proposed model is called the utterance-oriented Dirichlet pr...

A Context Clustering Technique for Average Voice Models

This paper describes a new context clustering technique for average voice model, which is a set of speaker independent speech synthesis units. In the technique, we first train speaker dependent models using multi-speaker speech database, and then construct a decision tree common to these speaker dependent models for context clustering. When a node of the decision tree is split, only the context...

Robust Speaker Clustering in Eigenspace

In this paper we propose a speaker clustering scheme working in ’Eigenspace’. Speaker models are transformed to a low-dimensional subspace using ’Eigenvoices’. For the speaker clustering procedure simple distance measures, e.g. Euklidean distance can be applied. Moreover, clustering can be accomplished with base models (for Eigenvoice projection) like Gaussian Mixture Models as well as conventi...