This paper presents a study of expressive speech synthesis applied to real-life application styles in Brazilian Portuguese. We explore the use of data with different recording conditions in state-of-the-art architectures in expressive TTS. Our results suggest that the variability of recording conditions of the same style, combined with a guided training of the latent representation space of the Reference Encoder, assists in the modeling of non-archetypal expressivities. Additionally, we propose an alternative to evaluating the model’s ability to generate expressive speech during preliminary results, based on a classifier using GeMAPS features.