Abstract: Growing individualization and higher numbers of
variants in industrial assembly products raise the complexity of
manufacturing processes. Technical assistance systems considering
both procedural and human factors allow for an increase in product
quality and a decrease in required learning times by supporting
workers with precise working instructions. Due to varying needs of
workers, the presentation of working instructions leads to several
challenges. This paper presents an approach for a multi-modal
visualization application to support assembly work of complex parts.
Our approach is integrated within an interconnected assistance system
network and supports the presentation of cloud-streamed textual
instructions, images, videos, 3D animations and audio files along
with multi-modal user interaction, customizable UI, multi-platform
support (e.g. tablet-PC, TV screen, smartphone or Augmented Reality
devices), automated text translation and speech synthesis. The worker
benefits from more accessible and up-to-date instructions presented
in an easy-to-read way.
Abstract: In this paper, we present a novel statistical approach to
corpus-based speech synthesis. Classically, phonetic information is
defined and considered as acoustic reference to be respected. In this
way, many studies were elaborated for acoustical unit classification.
This type of classification allows separating units according to their
symbolic characteristics. Indeed, target cost and concatenation cost
were classically defined for unit selection.
In Corpus-Based Speech Synthesis System, when using large text
corpora, cost functions were limited to a juxtaposition of symbolic
criteria and the acoustic information of units is not exploited in the
definition of the target cost.
In this manuscript, we token in our consideration the unit phonetic
information corresponding to acoustic information. This would be realized
by defining a probabilistic linguistic Bi-grams model basically
used for unit selection. The selected units would be extracted from
the English TIMIT corpora.