Mapping from Speech to Images Using Continuous State Space Models

Improved Bayesian Training for Context-Dependent Modeling in Continuous Persian Speech Recognition

Context-dependent modeling is a widely used technique for better phone modeling in continuous speech recognition. While different types of context-dependent models have been used, triphones have been known as the most effective ones. In this paper, a Maximum a Posteriori (MAP) estimation approach has been used to estimate the parameters of the untied triphone model set used in data-driven clust...

On images of continuous functions from a compact manifold to Euclidean space

Maximum-Likelihood Continuity Mapping (MALCOM): An Alternative to HMMs

We describe Maximum-Likelihood Continuity Mapping (MALCOM), an alternative to hidden Markov models (HMMs) for processing sequence data such as speech. While HMMs have a discrete "hidden" space constrained by a fixed finite-automaton architecture, MALCOM has a continuous hidden space-a continuity map-that is constrained only by a smoothness requirement on paths through the space. MALCOM fits int...

Fuzzy almost generalized $e$-continuous mappings

In this paper, we introduce and characterize the concept of fuzzy almost generalized $e$-continuous mappings. Several interesting properties of these mappings are also given. Examples and counter examples are also given to illustrate the concepts introduced in the paper. We also introduce the concept of fuzzy $f T_{frac{1}{2}}e$-space, fuzzy $ge$-space, fuzzy regular $ge$-space and  fuzzy gener...

Continuous Articulatory-to-Acoustic Mapping using Phone-based Trajectory HMM for a Silent Speech Interface

The article presents an HMM-based mapping approach for converting ultrasound and video images of the vocal tract into an audible speech signal, for a silent speech interface application. The proposed technique is based on the joint modeling of articulatory and spectral features, for each phonetic class, using Hidden Markov Models (HMM) and multivariate Gaussian distributions with full covarianc...