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.