We introduce a prototype system for modifying an arbitrary parameter of a speech signal. Unlike signal processing approaches that require dedicated methods for different parameters, our system can -in principle -modify any control parameter that the signal can be annotated with. Our system comprises three neural networks. The 'hider' removes all information related to the control parameter, outputting a hidden embedding. The 'finder' is an adversary used to train the 'hider', attempting to detect the value of the control parameter from the hidden embedding. The 'combiner' network recombines the hidden embedding with a desired new value of the control parameter. The input and output to the system are mel-spectrograms and we employ a neural vocoder to generate the output speech waveform. As a proof of concept, we use F0 as the control parameter. The system was evaluated in terms of control parameter accuracy and naturalness against a high quality signal processing method of F0 modification that also works in the spectrogram domain. We also show that, with modifications only to training data, the system is capable of modifying the 1 st and 2 nd vocal tract formants, showing progress towards universal signal modification.

Although few linguistic corpora are available in Yiddish, there are numerous sources of so-called “found data” that can be adapted for language research, pedagogy, and resource development. We describe the steps taken to create the first speech synthesis (text-to-speech) program in Yiddish. A state-of-the-art TTS model, FastSpeech 2, was trained on a hand-corrected data set consisting of literary texts paired with audio narrations by native speakers of the Polish and Lithuanian dialects. A quantitative evaluation by listeners found that the system produced speech that was both intelligible and natural-sounding. To demonstrate the system’s applications for language pedagogy, we offer a qualitative evaluation of Yiddish phonological features that are present or absent in a sample of synthesized recordings. We hope that the success of speech synthesis in Yiddish will inspire future projects to enable technological support for other minority languages in which transcribed recordings are available.