## 1 Introduction
### Goals of Our Research  
This paper describes an algorithm that automatically learns how to generate artificial voice by reproducing human voice through articulation synthesis and an optimization scheme. Our algorithm is therefore suitable for giving vocalization capabilities to talking robots by imitating the human learning process. With \"vacolization ability\" we refer not only to the generation of artificial voice, but also to the facial movements that accompany the generation. The algorithm requires that a human operator, as a caregiver, read a list of words aloud. The spoken words, analyzed at an acoustical and articulatory level, are automatically divided into pre-defined speech units and a knowledge base of such units is automatically built. Therefore, the algorithm can generate vocalizations from unrestricted text by concatenating the basic speech units. Other systems to generate artificial speech together with the related facial movements have been published (e.g., References [20; 21; 22]). Our algorithm, however, automatically generates artificial speech using the places of articulation estimated during a training phase. Optimization is realized using a genetic algorithm, on the basis of the similarity of the synthetic utterance with the original speech. In this work we estimate the face movements from the optimized degrees of membership of the places of articulation.In fact, there is a strong structural link between places of articulation and facial behavior during vocalization. The facial movements obtained were used to move a virtual face naturally during vocalization. It is worth noting that both artificial expressions and facial movements seem to clone that of the human operator who served as a virtual face caregiver, thus giving the opportunity to personalize the virtual head. It is important to note that we implemented the training phase on a GPU device so that the training phase is very fast. Time needed for training is only a few tenths of minutes needed to read a list of words. A general model of verbal/facial communication is shown in Figure 2. By adapting the Figure 2 to our talking head, the concept and message generation refer to a cognitive level, and the phonatory control refer to the vocalization level we are describing in this paper. We propose a novel design of a vocalization module which is the module that converts a written message into artificial speech and facial movement. Therefore, the issues we describe here are the following:  
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* articulatory vocalization of unrestricted text;
* generation of facial movement;
* synchronization of artificial speech with facial movements. Figure 2: General scheme of communication that is used by our algorithm. In talking head the articulatory configuration during vocalization is fundamental, because it could be used to control a mechanical talking face, such as described in Reference [29]. Our algorithm is similar to the process of human language acquisition. Indeed, in the early days of life, human infants try to imitate the speech they heard; this process is recognized as a language acquisition process driven by a mirror neuron system [30]. Similarly, our algorithm acquires vocalization through interaction with a human. It is important to note that it is not necessary to produce high quality artificial speech. Since our speech is used in a similarity comparison process, only low quality, yet intelligible speech, is enough. For this reason we use the simplest approach for artificial speech generation, that is the concatenation approach [31]. A number of basic vocal units is defined, and a base of knowledge of articulatory and acoustic parameters for their generation is built. Training iterates as long as the knowledge base is not full. The acoustic parameters and corresponding articulators are obtained with the fuzzy-genetic articulation model proposed in Reference [32], whose parameters are optimized to produce a language similar to that of a human user.