  Multi-speaker spoken datasets enable the creation of text-to-speech synthesis
(TTS) systems which can output several voice identities. The multi-speaker
(MSPK) scenario also enables the use of fewer training samples per speaker.
However, in the resulting acoustic model, not all speakers exhibit the same
synthetic quality, and some of the voice identities cannot be used at all.
  In this paper we evaluate the influence of the recording conditions, speaker
gender, and speaker particularities over the quality of the synthesised output
of a deep neural TTS architecture, namely Tacotron2. The evaluation is possible
due to the use of a large Romanian parallel spoken corpus containing over 81
hours of data. Within this setup, we also evaluate the influence of different
types of text representations: orthographic, phonetic, and phonetic extended
with syllable boundaries and lexical stress markings.
  We evaluate the results of the MSPK system using the objective measures of
equal error rate (EER) and word error rate (WER), and also look into the
distances between natural and synthesised t-SNE projections of the embeddings
computed by an accurate speaker verification network. The results show that
there is indeed a large correlation between the recording conditions and the
speaker's synthetic voice quality. The speaker gender does not influence the
output, and that extending the input text representation with syllable
boundaries and lexical stress information does not equally enhance the
generated audio across all speaker identities. The visualisation of the t-SNE
projections of the natural and synthesised speaker embeddings show that the
acoustic model shifts some of the speakers' neural representation, but not all
of them. As a result, these speakers have lower performances of the output
speech.
