In the modern world, speech technologies must be flexible and adaptable to any framework. Mass media globalization introduces multilingualism as a challenge for the most popular speech applications such as text-to-speech synthesis and automatic speech recognition. Mixed-language texts vary in their nature and when processed, some essential characteristics must be considered. In Spain and other Spanish-speaking countries, the use of Anglicisms and other words of foreign origin is constantly growing. A particularity of peninsular Spanish is that there is a tendency to nativize the pronunciation of non-Spanish words so that they fit properly into Spanish phonetic patterns. In our previous work, we proposed to use hand-crafted nativization tables that were capable of nativizing correctly 24% of words from the test data. In this work, our goal was to approach the nativization challenge by data-driven methods, because they are transferable to other languages and do not drop in performance in comparison with explicit rules manually written by experts. Training and test corpora for nativization consisted of 1000 and 100 words respectively and were crafted manually. Different specifications of nativization by analogy and learning from errors focused on finding the best nativized pronunciation of foreign words. The best obtained objective nativization results showed an improvement from 24% to 64% in word accuracy in comparison to our previous work. Furthermore, a subjective evaluation of the synthesized speech allowed for the conclusion that nativization by analogy is clearly the preferred method among listeners of different backgrounds when comparing to previously proposed methods. These results were quite encouraging and proved that even a small training corpus is sufficient for achieving significant improvements in naturalness for English inclusions of variable length in Spanish utterances.

Peer Reviewed