Training Augmented Models Using SVMs

نویسندگان

  • Mark J. F. Gales
  • Martin I. Layton
چکیده

There has been significant interest in developing new forms of acoustic model, in particular models which allow additional dependencies to be represented than those contained within a standard hidden Markov model (HMM). This paper discusses one such class of models, augmented statistical models. Here, a local exponential approximation is made about some point on a base model. This allows additional dependencies within the data to be modelled than are represented in the base distribution. Augmented models based on Gaussian mixture models (GMMs) and HMMs are briefly described. These augmented models are then related to generative kernels, one approach used for allowing support vector machines (SVMs) to be applied to variable length data. The training of augmented statistical models within an SVM, generative kernel, framework is then discussed. This may be viewed as using maximum margin training to estimate statistical models. Augmented Gaussian mixture models are then evaluated using rescoring on a large vocabulary speech recognition task. key words: speech recognition, hidden Markov models, support vector machines, augmented statistical models

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

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Svms, Score-spaces and Maximum Margin Statistical Models

There has been significant interest in developing new forms of acoustic model, in particular models which allow additional dependencies to be represented than allowed within a standard hidden Markov model (HMM). This paper discusses one such class of models, augmented statistical models. Here a locally exponential approximation is made about some point on a base distribution. This allows additi...

متن کامل

Faster Training of Structural SVMs with Diverse M-Best Cutting-Planes

Training of Structural SVMs involves solving a large Quadratic Program (QP). One popular method for solving this optimization problem is a cutting-plane approach, where the most violated constraint is iteratively added to a working-set of constraints. Unfortunately, training models with a large number of parameters remains a time consuming process. This paper shows that significant computationa...

متن کامل

DivMCuts: Faster Training of Structural SVMs with Diverse M-Best Cutting-Planes

Training of Structural SVMs involves solving a large Quadratic Program (QP). One popular method for solving this QP is a cutting-plane approach, where the most violated constraint is iteratively added to a working-set of constraints. Unfortunately, training models with a large number of parameters remains a time consuming process. This paper shows that significant computational savings can be a...

متن کامل

Support Vector Machines as Acoustic Models in Speech Recognition

Speech recognition is usually based on Hidden Markov Models (HMMs), which represent the temporal dynamics of speech very efficiently, and Gaussian mixture models, which do non-optimally the classification (acoustic modeling) of speech into single speech units (phonemes). In this paper we present an overview about the use of Support Vector Machines (SVMs) for the classification task by integrati...

متن کامل

Structured Support Vector Machines for Speech Recognition

Discriminative training criteria and discriminative models are two ešective improvements for HMM-based speech recognition. is thesis proposed a structured support vector machine (SSVM) framework suitable for medium to large vocabulary continuous speech recognition. An important aspect of structured SVMs is the form of features. Several previously proposed features in the eld are summarized in ...

متن کامل

ذخیره در منابع من


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

عنوان ژورنال:
  • IEICE Transactions

دوره 89-D  شماره 

صفحات  -

تاریخ انتشار 2006