Intensity of the Reader’s Voice in the Reading Aloud of Fiction: Effects of the Character’s Gender

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The speaker’s gender is a crucial factor affecting the acoustic features of the voice. One such feature is voice intensity, also known as sound pressure level (SPL). Previous studies have indicated that the female voice may involve lower values of SPL than the male voice. Moreover, there are suggestions that the variability of voice intensity tends to be lower for women than for men as well.

The major aim of this paper is to examine the effects of literary character’s gender on the reader’s SPL, measured in decibels (dB), and the variability of voice intensity, measured as the standard deviation (SD) of SPL, while reading prose aloud. The secondary aims are to investigate the general shifts of SPL and SD of SPL in dialogues independently of other variables and to consider the possible effects of the reader’s gender and the reader’s dialect. In order to accomplish these tasks, a representative sample of dialogue excerpts with male and female characters was used. Each fragment was located in the corresponding audiobook and analysed in terms of the two acoustic features under discussion. Typical values of SPL and the SD of SPL for different readers were measured in the entire chapters from which fragments were selected and the results were compared with those obtained from the extracts. In this way, it was possible to establish the relative shifts of SPL and the SD of SPL for each of the analysed fragments.

Contrary to what had been expected, a statistical analysis of the results revealed no effects of the character’s gender on any of the response variables. However, conclusions concerning secondary aims were more definitive. A general trend to decrease the SD of SPL in dialogues in comparison to the rest of the text in a novel was observed. This tendency is independent of any of the factors included in the study. It was also observed that male American readers tend to lower their voice intensity when reading dialogues. All these findings may be applied in developing text to speech software.

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