Several works attempted to address the problem. In general, these employ simple frame-level presentations and a variety of classification algorithms. Typically, individual events are modelled as Hidden Markov Models (HMM), and a speech recognition framework is employed to detect them [4]. The audio segments can also be characterized by the Gaussian population histograms derived from a Gaussian ...

A hybrid Hidden Markov Model (HMM) Gaussian Mixture Model (GMM) system was proposed to automatically select tokens of /iau/, /ai/, /ei/, /m/ and /n/ in a database of recordings of Standard-Chinese speech collected under studio-clean, mobile-landline degraded and mismatched recording conditions. The FVC systems constructed were all MFCC GMM-UBM systems, but based on different portions of the rec...

The hidden Markov model (HMM), used with Gaussian Process (GP) as an emission model, has been widely to sequential data in complex form. This study introduces the hybrid Bayesian HMM GP using SM kernel (HMM-GPSM) estimate state of each time-series observation, that is, sequentially observed from a single channel. We then propose scalable inference method train HMM-GPSM large-scale sequences dat...

Human endogenous retroviruses (HERVs) are remnants of ancient retrovirus infections and now reside within the human DNA. Recently HERV expression has been detected in both normal and diseased tissues. However, the patterns of expression of individual HERV sequences are mostly unknown. In this work we use a generative mixture model, based on hidden Markov models, for estimating the activities of...

This tutorial treats mixtures of Gaussian probability distribution functions. Gaussian mixtures are combinations of a finite number of Gaussian distributions. They are used to model complex multidimensional distributions. When there is a need to learn the parameters of the Gaussian mixture, the EM algorithm is used. In the second part of this tutorial mixtures of Gaussian are used to model the ...

Language Identification is process of identifying the language being spoken from a sample of speech by an unknown speaker. Most of the previous work in this field is based on the fact that phoneme sequences have different occurrence probabilities in different languages, and all the systems designed till now have tried to exploit this fact. Language identification process in turn consists of two...

This tutorial treats mixtures of Gaussian probability distribution functions. Gaussian mixtures are combinations of a finite number of Gaussian distributions. They are used to model complex multi-dimensional distributions. When there is a need to learn the parameters of the Gaussian mixture, the EM algorithm is used. In the second part of this tutorial mixtures of Gaussian are used to model the...

In conventional Gaussian mixture based Hidden Markov Model (HMM), all states are usually modeled with a uniform, fixed number of Gaussian kernels. In this paper, we propose to allocate kernels nonuniformly to construct a more parsimonious HMM. Different number of Gaussian kernels are allocated to states in a non-uniform and parsimonious way so as to optimize the Minimum Description Length (MDL)...

In this paper, we present a simple and efficient feature modeling approach for tracking the pitch of two speakers speaking simultaneously. We model the spectrogram features using Gaussian Mixture Models (GMMs) in combination with the Minimum Description Length (MDL) model selection criterion. This enables to automatically determine the number of Gaussian components depending on the available da...

Acoustic modeling based on Hidden Markov Models (HMMs) is employed by state-of-theart stochastic speech recognition systems. In continuous density HMMs, the state scores are computed using Gaussian mixture models. On the other hand, Deep Neural Networks (DNN) can be used to compute the HMM state scores. This leads to significant improvement in the recognition accuracy. Conditional Random Fields...