A Latent Variable Modelling Approach to the Acoustic-to-articulatory Mapping Problem

نویسندگان

  • Miguel Á. Carreira-Perpiñán
  • Steve Renals
چکیده

We present a latent variable approach to the acoustic-toarticulatory mapping problem, where different vocal tract configurations can give rise to the same acoustics. In latent variable modelling, the combined acoustic and articulatory data are assumed to have been generated by an underlying low-dimensional process. A parametric probabilistic model is estimated and mappings are derived from the respective conditional distributions. This has the advantage over other methods, such as articulatory codebooks or neural networks, of directly addressing the nonuniqueness problem. We demonstrate our approach with electropalatographic and acoustic data from the ACCOR database.

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

Acoustic-to-Articulatory Mapping Based on Mixture of Probabilistic Canonical Correlation Analysis

In this paper, we propose a novel acoustic-to-articulatory mapping model based on mixture of probabilistic canonical correlation analysis (mPCCA). In PCCA, it is assumed that two different kinds of data are observed as results from different linear transforms of a common latent variable. It is expected that this variable represents a common factor which is inherent in the different domains, suc...

متن کامل

Dimensionality reduction of electropalatographic data using latent variable models

We consider the problem of obtaining a reduced dimension representation of electropalatographic (EPG) data. An unsupervised learning approach based on latent variable modelling is adopted, in which an underlying lower dimension representation is inferred directly from the data. Several latent variable models are investigated, including factor analysis and the generative topographic mapping (GTM...

متن کامل

Towards articulatory speech recognition: learning smooth maps to recover articulator information

We present a novel method for recovering articulator movements from speech acoustics based on a constrained form [9] of a hidden Markov model. The model attempts to explain sequences of high dimensional data using smooth and slow trajectories in a latent variable space. The key insight is that this continuity constraint when applied to speech helps to solve the \ill-posed" problem of acoustic t...

متن کامل

Acoustic-to-Articulatory Inversion Mapping Based on Latent Trajectory Gaussian Mixture Model

A maximum likelihood parameter trajectory estimation based on a Gaussian mixture model (GMM) has been successfully implemented for acoustic-to-articulatory inversion mapping. In the conventional method, GMM parameters are optimized by maximizing a likelihood function for joint static and dynamic features of acoustic-articulatory data, and then, the articulatory parameter trajectories are estima...

متن کامل

A Speaker Adaptive DNN Training Approach for Speaker-Independent Acoustic Inversion

We address the speaker-independent acoustic inversion (AI) problem, also referred to as acoustic-to-articulatory mapping. The scarce availability of multi-speaker articulatory data makes it difficult to learn a mapping which generalizes from a limited number of training speakers and reliably reconstructs the articulatory movements of unseen speakers. In this paper, we propose a Multi-task Learn...

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 1999