# Generating Robotic Speech Prosody for Human Robot Interaction: A Preliminary Study
## 5 Discussion  
Considering the importance of prosodic vocal speech in robotic applications, and based on the low capability of elderly people to perceive weak pitch contours, this work suggests a method that is able to filter information into a more perceivable pitch without changing its original emotional characteristics, as well as presents the results of preliminary experiments regarding the prosodic vocal speech of robots. This study generated robotic speech for two cases; in one case, only information is delivered, and, in the other case, certain information is delivered in addition to natural small talk. Three emotions were used in the generated voices, and we have validated the robotic voices. The results showed that the speech with small talk provided better predictions. The user-study in this paper introduced two experimental results for investigating the impact of prosody in human robot interactions. The result of experiment A showed that users understood the information better when NAO spoke with a positive or negative voice than with a neutral voice, which supported **H1**. The second result, given in experiment A, implied that the information can be easy to understand with all voices, but did not reflect emotions to the users, and thus **H2** was not proved by the experiment. Another result described in experiment B partially supported **H2**, where the negative voice reflected on the emotional states of listeners. **H1** was not revealed in the experiment. One of limitations in this study is the small sample size. The experiment B had three participants, which is insufficient to statistically validate the result. Moreover, as a preliminary study, the subjects in experiment A and B were only university students, and it will be necessary to collect data using larger age range of people as participants. The findings in this paper could be applied to a speech prosody model in the future. To alter the pitch contour to be perceived more easily by the elderly while still maintaining the original emotional characteristics, first, a voice database is used, such as that used in Reference [41]. Each set of the database should be related to an emotional markup language. This can be done using information clustering as proposed in Reference [22] or by manual tagging. In this way, each piece of data is related to a specific emotional state. Although this structure is presented as a combination of two sets of information, it can also be implemented as one dataset of emotional speech. The basic idea for the system is to find a set of prosody in a voice database that retains the emotional value with voice parameters under the limitation presented by elderly users. This specific group of elderly-oriented speech has its characteristics used by a filter in order to create new and equally successful speech prosody, even with the above cited limitations in the voice characteristics. The voice information is sent to a classifier, which will correlate the speech features according to emotional markup information. Component analysis is used as a classifier technique to separate and analyze aspects inside the original data that are difficult to observe with normal correlation or density analysis. The classifier clusters the voices present in the database according to its recognition level, taking into consideration the hearing limitation presented by the elderly. It is also possible to use different degrees of limitations. This clustered information is used by a filter which learns what kinds of changes are necessary for the voice parameters to express a specific input motion and text. The filter component is based on an HMM speech synthesizer, which will create prosody with strong pitch contours while maintaining the original emotional characteristics. During the training stage of the filter, this process can be performed several times, changing the characteristics of the classifier and the filter in order to achieve a final desired speech output. The optimum values and details for the filter are under development.