در کاربرد تشخیص زبان گفتاری GMM-VSM در قالب سیستم GMM

نویسندگان

  • قاسمیان , فهیمه دانشکده مهندسی کامپیوتر و فناوری اطلاعات، دانشگاه صنعتی امیرکبیر
  • همایون‌پور , محمدمهدی دانشکده مهندسی کامپیوتر و فناوری اطلاعات، دانشگاه صنعتی امیرکبیر
چکیده مقاله:

GMM is one of the most successful models in the field of automatic language identification. In this paper we have proposed a new model named adapted weight GMM (AW-GMM). This model is similar to GMM but the weights are determined using GMM-VSM LID system based on the power of each component in discriminating one language from the others. Also considering the computational complexity of GMM-VSM, we have proposed a technique for constructing bigram sequences of components which could be used for higher sequence orders and decreases the complexity. Experiments on four languages of OGI corpus including English, Farsi, French and German have shown the effectiveness of proposed techniques.

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Comparison Comparison PCA Train GMM Feature Reduction Classify GMM Threshold

A new approach to face verification from 3D data is presented. The method uses 3D registration techniques designed to work with resolution levels typical of the irregular point cloud representations provided by Structured Light scanning. Preprocessing using a-priori information of the human face and the Iterative Closest Point algorithm are employed to establish correspondence between test and ...

متن کامل

Gaussian Multipole Model (GMM).

An electrostatic model based on charge density is proposed as a model for future force fields. The model is composed of a nucleus and a single Slater-type contracted Gaussian multipole charge density on each atom. The Gaussian multipoles are fit to the electrostatic potential (ESP) calculated at the B3LYP/6-31G* and HF/aug-cc-pVTZ levels of theory and tested by comparing electrostatic dimer ene...

متن کامل

Single-tree GMM training

In this short document, we derive a tree-independent single-tree algorithm for Gaussian mixture model training, based on a technique proposed by Moore [8]. Here, the solution we provide is tree-independent and thus will work with any type of tree and any type of traversal; this is more general than Moore’s original formulation, which was limited to mrkd-trees. This allows us to develop a flexib...

متن کامل

Tunable GMM Kernels

The recently proposed “generalized min-max” (GMM) kernel [9] can be efficiently linearized, with direct applications in large-scale statistical learning and fast near neighbor search. The linearized GMM kernel was extensively compared in [9] with linearized radial basis function (RBF) kernel. On a large number of classification tasks, the tuning-free GMM kernel performs (surprisingly) well comp...

متن کامل

Gmm-free Dnn Training

While deep neural networks (DNNs) have become the dominant acoustic model (AM) for speech recognition systems, they are still dependent on Gaussian mixture models (GMMs) for alignments both for supervised training and for context dependent (CD) tree building. Here we explore bootstrapping DNN AM training without GMM AMs and show that CD trees can be built with DNN alignments which are better ma...

متن کامل

Implementing the Infinite GMM

Rasmussen [2000] describes a hierarchical Bayesian model for a mixture of Gaussians with a possibly infinite number of components. I have implemented his model for univariate data, along with the Adaptive Rejection Sampling method of Gilks and Wild [1992]. In this paper I explain some of the difficulties in implementing Rasmussen’s model and clarify some of the points he leaves vague in his pap...

متن کامل

منابع من

با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ذخیره در منابع من قبلا به منابع من ذحیره شده

{@ msg_add @}


عنوان ژورنال

دوره 2  شماره 5

صفحات  1- 8

تاریخ انتشار 2011-01

با دنبال کردن یک ژورنال هنگامی که شماره جدید این ژورنال منتشر می شود به شما از طریق ایمیل اطلاع داده می شود.

کلمات کلیدی

کلمات کلیدی برای این مقاله ارائه نشده است

میزبانی شده توسط پلتفرم ابری doprax.com

copyright © 2015-2023