  Deep learning enables the development of efficient end-to-end speech
processing applications while bypassing the need for expert linguistic and
signal processing features. Yet, recent studies show that good quality speech
resources and phonetic transcription of the training data can enhance the
results of these applications. In this paper, the RECOApy tool is introduced.
RECOApy streamlines the steps of data recording and pre-processing required in
end-to-end speech-based applications. The tool implements an easy-to-use
interface for prompted speech recording, spectrogram and waveform analysis,
utterance-level normalisation and silence trimming, as well grapheme-to-phoneme
conversion of the prompts in eight languages: Czech, English, French, German,
Italian, Polish, Romanian and Spanish.
  The grapheme-to-phoneme (G2P) converters are deep neural network (DNN) based
architectures trained on lexicons extracted from the Wiktionary online
collaborative resource. With the different degree of orthographic transparency,
as well as the varying amount of phonetic entries across the languages, the
DNN's hyperparameters are optimised with an evolution strategy. The phoneme and
word error rates of the resulting G2P converters are presented and discussed.
The tool, the processed phonetic lexicons and trained G2P models are made
freely available.
