Phoneme dedicated ANN improves segmental duration model

The Phoneme Dedicated Artificial Neural Network (PDANN) segmental duration model consists of a set of ANNs trained specifically for each phoneme segment in order to avoid miscellaneous influence of different types of phoneme segments. Therefore, each ANN is dedicated to predict the duration of a specific phoneme segment. Objective and subjective measurements of the performance of the PDANN model were compared with those of a typical ANN model using the same input features and database. The results indicate a slight, but clear, perceptually perceived preference towards the PDANN

Use of phoneme dedicated artificial neural networks to predict segmental durations

The results of two alternative models to predict segmental durations in speech synthesis, both based on Artificial Neural Networks (ANNs) are discussed. The ANN model consists in just one ANN trained to predict the segmental durations for all phonemes. The phoneme dedicated ANN model consists in a set of ANNs, each one dedicated to predict the segmental duration of a specific phoneme. Both models are compared with the same input information extracted from one European Portuguese database. Objective and subjective measurements of performance of both approaches are compared. A slight preference was denoted for the phoneme dedicated ANN model

Evaluation of a neural network segmental duration model for Portuguese

This paper presents a segmental duration model, that, as far as the authors know, is the first published for European Portuguese, with objective and subjective evaluations. The model is aimed at TTS applications and is based on an ANN, trained with a resilient back-propagation algorithm. Using a substantial amount of training data and a carefully selected set of input factors, the standard deviation of the error of segmental duration estimations reaches 19 ms and the correlation coefficient goes above 0.9. Several models have been published for other languages with objective and subjective good performances. The methodology of construction of the model, the importance of the used factors and the neural network will be presented, together with the evaluation of the model, allowing a comparison with other models for other languages

A new multi-modal database for developing speech recognition systems for an assistive technology application

In this paper we report on the acquisition and content of a new database intended for developing audio-visual speech recognition systems. This database supports a speaker dependent continuous speech recognition task, based on a small vocabulary, and was captured in the European Portuguese language. Along with the collected multi-modal speech materials, the respective orthographic transcription and time-alignment files are supplied. The package also includes data on stochastic language models and the generative grammar associated to the collected spoken sentences. The application addressed by this database, which consists of voice control of a basic scientific calculator, has the particularity of being designed for a person with a specific motor impairment, namely muscular dystrophy. This specificity is a remarkable characteristic, given the lack of such kind of data resources for developing assistive systems based on audio-visual speech recognition technology

Indirect parameter estimation of continuous-time systems using discrete time data

This paper addresses the problem of parameter estimation of continuous-time systems using samples of its input-output data. We propose a method based on the bilinear transformation to obtain an equivalent discrete-time model. Introducing a new polynomial pre-filter it .is possible to compute the physical parameters via inverse mapping between the discrete-time and the continuous-time models. A simulation example is given to illustrate the noise effects in the parameter estimation results. Using experimental results, we demonstrate the ability of the estimator. to handle real measurement problems

A low cost solution for laboratory experiments in induction motor control

In this paper we present a controller suitable for educational activities in electric drives. A prototype has been designed specifically to meet the requirement of low cost and it contains all of the active functions required to implement the open loop control of an induction motor. In this way, the prototype allows the easy assimilation of important concepts and enables the understanding of the enclosed subsystems. Some experiments that highlight the quality of the proposed approach are presented

A boot-strap estimator for joint flux and parameters online identification for vector controlled induction motor drives

This paper presents a new approach for joint rotor flux and electrical parameters on-line identification in vector controlled high-performance induction motor drives based on a boot-strap estimator that uses a reduced order extended Kalman filter for rotor flux components and rotor parameters estimation and a recursive prediction error method for stator parameters estimation. Within the prediction error method some approaches are used and compared that affect both the adaptation gain and the direction in which the updates of stator parameters are made. The induction motor model structures are described in the rotor reference frame in order to reduce the computational effort by using a higher sampling time interval

Classes of model structures for state and parameter identification of vector controlled induction machines

The purpose of this paper is to present a synthesis of classes of model structures for joint state and parameter identification of vector controlled induction motors for real time and normal operating conditions. Based on its classical model a set of new classes of model structures is discussed and proposed for simultaneous estimation of rotor flux components and electrical parameters

Modelling and simulation of power electronic systems using a bond graph formalism

This paper deals with the modelling of power electronic systems using the bond graph formalism. The switching components are modelled using an ideal representation so that a constant topology system is obtained. The purpose of the present contribution is to discuss a technique that combines bond graph energy-flow modelling and signal-flow modelling schemes for simulation and prototyping of signal processing algorithms in power electronics systems. In this paper, we will discuss models of the use of fully-controlled, semi-controlled and non-controlled switches in the field of power static converters. By concept, a simulation environment can be examined at different abstraction or hierarchy levels. The approach in this paper is, accordingly, the formulation of a simulation task at different levels: component level, topology level, functional description and implementation description. The paper concludes with two practical examples of simulation of power electronics systems