Text-independent speaker recognition systems such as those based on Gaussian mixture models (GMMs) do not include time sequence information (TSI) within the model itself. The level of importance of TSI in speaker recognition is an interesting question and one addressed in this paper. Recent works has shown that the utilisation of higher-level information such as idiolect, pronunciation, and pro...

It is often useful to fit a probability model to a data collection, in order to concisely represent the data, to feed learning algorithms that work on densities, to extract features or, simply, to uncover underlying structures. A particularly popular probability model is the Gaussian Mixture Model (GMM). Among many other applications, GMM form a central tool to build time-frequency models of au...

This paper derives explicit expressions for the asymptotic variances of the maximum likelihood and continuously updated GMM estimators under potentially misspecified models. The proposed misspecification-robust variance estimators allow the researcher to conduct valid inference on the model parameters even when the model is rejected by the data. Although the results for the maximum likelihood e...

nowadays, audio and video media data is already facilitates generation, transmission, storage and circulation on the global scale. Audio and video data is geometrically fast as the rate of growth, the video data processing and analysis have lagged behind the pace of development in the growth of data, resulting in large amounts of data is wasted. Therefore, it becomes an urgent need for efficien...

In most speaker recognition systems speech utterances are not constrained in content or language. In a text-dependent speaker recognition system lexical content of speech and language are known in advance. The goal of this paper is to show that this information can be used by a segmental features (SF) approach to improve a standard Gaussian mixture model with MFCC features (GMM-MFCC). Speech fe...

We introduce a new regularized nonnegative matrix factorization (NMF) method for supervised single-channel source separation (SCSS). We propose a new multi-objective cost function which includes the conventional divergence term for the NMF together with a prior likelihood term. The first term measures the divergence between the observed data and the multiplication of basis and gains matrices. T...

Model management is essential for coping with the complexity introduced by the increasing number and varied nature of artifacts involved in MDE-based projects. Global Model Management (GMM) addresses this issue enabling the representation of artifacts, particularly transformation composition and execution, by a model called a megamodel. Typing information about artifacts can be used for prevent...

We propose a low-memory-bandwidth, high-efficiency VLSI architecture for 60-k word real-time continuous speech recognition. Our architecture includes a cache architecture using the locality of speech recognition, beam pruning using a dynamic threshold, two-stage language model searching, a parallel Gaussian Mixture Model (GMM) architecture based on the mixture level and frame level, a parallel ...