Internet of Things plays a key role in our lives today from managing airport passenger traffic, smart houses and cities to taking care of the elderly, it aims to improve life in all areas, and the technological development we are seeing has contribut
ed to a wide spread in many domains.
Platforms are the supporting software that connects everything within the Internet of things system. The platform facilitates communication, data flow, device management, and application functionality.
The Thinger.io platform is an easy-to-use platform that provides a variety of services to users. The platform enables communication of various types of devices and chipsets.
The idea was to create a personal assistant that works via voice commands to control devices connected to the Thinger.io platform remotely over the Internet in real time, The aim of adding this possibility to the platform is to make it simpler to allow anyone of any age or experience to use it to facilitate their life the way they choose, whereas The Vinus Assistant - as we called it - has the flexibility, reliability and functionality to deal with any application.
In this paper we describe a cepstral model of the vocal tract which models both formants and antiformants.
The investigated model is more precise compared to the linear prediction model, which models
only the formants of the vocal tract. The expone
ntial function is used for the inverse transformation.
However, it is difficult to implement this function on a digital signal processor. To solve this issue we use a
continued fraction expansion to approximate the exponential function. The transfer function that
approximates the exponential function is realized by using the Infinite Impulse Response (IIR) digital
filter, in which branches type Finite Impulse Response (FIR) digital filters are included. The coefficients
of the FIR digital filters are just the coefficients of the real speech cepstrum. The state-space difference
equations are proposed and implemented on a DSP56300 fixed-point digital signal processor (Motorola).
Finally, the results of the digital signal processor implementation for chosen vowels and consonants are
evaluated.
The main purpose of the present research is to support Arabic Text- to - Speech synthesizers, with
natural prosody, based on linguistic analysis of texts to synthesize, and automatic prosody generation,
using rules which are deduced from recorded s
ignals analysis, of different types of sentences in Arabic. All
the types of Arabic sentences (declarative and constructive) were enumerated with the help of an expert in
Arabic linguistics . A textual corpus of about 2500 sentences covering most of these types was built and
recorded both in natural prosody and without prosody. Later, these sentences were analyzed to extract
prosody effect on the signal parameters, and to build prosody generation rules. In this paper, we present
the results on negation sentences, applied on synthesized speech using the open source tool MBROLA. The
results can be used with any parametric Arabic synthesizer. Future work will apply the rules on a new
Arabic synthesizer based on semi-syllables units, which is under development in the Higher Institute for
Applied Sciences and Technology.
