We propose a new approach for phoneme mapping in cross-lingual transfer learning for text-to-speech (TTS) in under-resourced languages (URLs), using phonological features from the PHOIBLE database and a language-independent mapping rule. This approach was validated through our experiment, in which we pre-trained acoustic models in Dutch, Finnish, French, Japanese, and Spanish, and fine-tuned them with 30 minutes of Frisian training data. The experiment showed an improvement in both naturalness and pronunciation accuracy in the synthesized Frisian speech when our mapping approach was used. Since this improvement also depended on the source language, we then experimented on finding a good criterion for selecting source languages. As an alternative to the traditionally used language family criterion, we tested a novel idea of using Angular Similarity of Phoneme Frequencies (ASPF), which measures the similarity between the phoneme systems of two languages. ASPF was empirically confirmed to be more effective than language family as a criterion for source language selection, and also to affect the phoneme mapping’s effectiveness. Thus, a combination of our phoneme mapping approach and the ASPF measure can be beneficially adopted by other studies involving multilingual or cross-lingual TTS for URLs.

We developed a bilingual Frisian/Dutch speech recognizer for council meetings in Fryslân (the Netherlands). During these meetings both Frisian and Dutch are spoken, and code switching between both languages shows up frequently. The new speech recognizer is based on an existing speech recognizer for Frisian and Dutch named FAME!, which was trained and tested on historical radio broadcasts. Adapting a speech recognizer for the council meeting domain is challenging because of acoustic background noise, speaker overlap and the jargon typically used in council meetings. To train the new recognizer, we used the radio broadcast materials utilized for the development of the FAME! recognizer and added newly created manually transcribed audio recordings of council meetings from eleven Frisian municipalities, the Frisian provincial council and the Frisian water board. The council meeting recordings consist of 49 hours of speech, with 26 hours of Frisian speech and 23 hours of Dutch speech. Furthermore, from the same sources, we obtained texts in the domain of council meetings containing 11 million words; 1.1 million Frisian words and 9.9 million Dutch words. We describe the methods used to train the new recognizer, report the observed word error rates, and perform an error analysis on remaining errors.

We present a new speech database containing 18.5 hours of annotated radio broadcasts in the Frisian language. Frisian is mostly spoken in the province Fryslan and it is the second official language of the Netherlands. The recordings are collected from the archives of Omrop Fryslan, the regional public broadcaster of the province Fryslan. The database covers almost a 50-year time span. The native speakers of Frisian are mostly bilingual and often code-switch in daily conversations due to the extensive influence of the Dutch language. Considering the longitudinal and code-switching nature of the data, an appropriate annotation protocol has been designed and the data is manually annotated with the orthographic transcription, speaker identities, dialect information, code-switching details and background noise/music information.