Zofia Malisz

Zofia Malisz, Jan Foremski, Małgorzata Kul

We present a speech database and a phoneme-level language model of Polish. The database and model are designed for the analysis of prosodic and discourse factors and their impact on acoustic parameters in interaction with predictability effects. The database is also the first large, publicly available Polish speech corpus of excellent acoustic quality that can be used for phonetic analysis and training of multi-speaker speech technology systems. The speech in the database is processed in a pipeline that achieves a 90% degree of automation. It incorporates state-of-the-art, freely available tools enabling database expansion or adaptation to additional languages.

Gustavo Teodoro Döhler Beck, Ulme Wennberg, Zofia Malisz et al.

Deep learning has revolutionised synthetic speech quality. However, it has thus far delivered little value to the speech science community. The new methods do not meet the controllability demands that practitioners in this area require e.g.: in listening tests with manipulated speech stimuli. Instead, control of different speech properties in such stimuli is achieved by using legacy signal-processing methods. This limits the range, accuracy, and speech quality of the manipulations. Also, audible artefacts have a negative impact on the methodological validity of results in speech perception studies. This work introduces a system capable of manipulating speech properties through learning rather than design. The architecture learns to control arbitrary speech properties and leverages progress in neural vocoders to obtain realistic output. Experiments with copy synthesis and manipulation of a small set of core speech features (pitch, formants, and voice quality measures) illustrate the promise of the approach for producing speech stimuli that have accurate control and high perceptual quality.