Automatic Date Fruit Recognition Using Outlier Detection Techniques and Gaussian Mixture Models

Driver Recognition Using Gaussian Mixture Models and Decision Fusion Techniques

In this paper we present our research in driver recognition. The goal of this study is to investigate the performance of different classifier fusion techniques in a driver recognition scenario. We are using solely driving behavior signals such as break and accelerator pedal pressure, engine RPM, vehicle speed, steering wheel angle for identifying the driver identities. We modeled each driver us...

Automatic Speaker Recognition Using Gaussian Mixture Speaker Models

• Speech conveys several levels of information. On a primary level, speech conveys the words or message being spoken, but on a secondary level, speech also reveals information about the speaker. The Speech Systems Technology group at Lincoln Laboratory has developed and experimented with approaches for automatically recognizing the words being spoken, the language being spoken, and the topic of...

Automatic detection of snoring events using Gaussian mixture models

Automatic Accent Identification Using Gaussian Mixture Models

It is well known that speaker variability caused by accent is an important factor in speech recognition. Some major accents in China are so different as to make this problem very severe. In this paper, we propose a Gaussian mixture model (GMM) based Mandarin accent identification method. In this method, a number of GMMs are trained to identify the most likely accent given test utterances. The i...

Subspace Gaussian Mixture Models for Automatic Speech Recognition

In most of state-of-the-art speech recognition systems, Gaussian mixture models (GMMs) are used to model the density of the emitting states in the hidden Markov models (HMMs). In a conventional system, the model parameters of each GMM are estimated directly and independently given the alignment. This results a large number of model parameters to be estimated, and consequently, a large amount of...