Validating rt-MRI Based Articulatory Representations via Articulatory Recognition

نویسندگان

  • Athanasios Katsamanis
  • Erik Bresch
  • Vikram Ramanarayanan
  • Shrikanth S. Narayanan
چکیده

The large corpus of real time magnetic resonance image sequences of the vocal tract during speech production that was recently acquired and can be referred to as MRI-TIMIT, provides us with a unique platform for systematically studying articulatory dynamics. Compared to previously collected articulatory datasets, e.g., using articulography or X-rays, MRI-TIMIT is a rich source of information for the entire vocal tract and not only for certain articulatory landmarks and further has the potential to continue increasing in size covering a large variety of speakers and speaking styles. In this work, we investigate an articulatory representation based on full vocal tract shapes. We employ an articulatory recognition framework in MRI-TIMIT to analyze its merits and drawbacks. We argue that articulatory recognition can serve as a general validation tool for real-time MRI based articulatory representations.

برای دانلود 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

Automatic Data-Driven Learning of Articulatory Primitives from Real-Time MRI Data Using Convolutive NMF with Sparseness Constraints

We present a procedure to automatically derive interpretable dynamic articulatory primitives in a data-driven manner from image sequences acquired through real-time magnetic resonance imaging (rt-MRI). More specifically, we propose a convolutive Nonnegative Matrix Factorization algorithm with sparseness constraints (cNMFsc) to decompose a given set of image sequences into a set of basis image s...

متن کامل

Combining acoustic and articulatory feature information for robust speech recognition

The idea of using articulatory representations for automatic speech recognition (ASR) continues to attract much attention in the speech community. Representations which are grouped under the label ‘‘articulatory’’ include articulatory parameters derived by means of acoustic-articulatory transformations (inverse filtering), direct physical measurements or classification scores for pseudo-articul...

متن کامل

Multiview Representation Learning via Deep CCA for Silent Speech Recognition

Silent speech recognition (SSR) converts non-audio information such as articulatory (tongue and lip) movements to text. Articulatory movements generally have less information than acoustic features for speech recognition, and therefore, the performance of SSR may be limited. Multiview representation learning, which can learn better representations by analyzing multiple information sources simul...

متن کامل

Integrating Articulatory Features into Acoustic Models for Speech Recognition

It is often assumed that acoustic-phonetic or articulatory features can be beneficial for automatic speech recognition (ASR), e.g. because of their supposedly greater noise robustness or because they provide a more convenient interface to higher-level components of ASR systems such as pronunciation modeling. However, the success of these features when used as an alternative to standard acoustic...

متن کامل

Pseudo-Articulatory Representations and the Use of Syllable Structure for Speech Recognition

The alternative approach for speech recognition proposed here is based on pseudo-articulatory representations (PARs), which can be described as approximation of distinctive features, and aims to establish a mapping between them and their acoustic specifications. This mapping which is used as the basis for recognition is first done for vowels. It is obtained using multiple regression analysis af...

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

برای دانلود 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

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2011