Detecting acronyms from capital letter sequences in Spanish

This paper presents an automatic strategy to decide how to pronounce a Capital Letter Sequence (CLS) in a Text to Speech system (TTS). If CLS is well known by the TTS, it can be expanded in several words. But when the CLS is unknown, the system has two alternatives: spelling it (abbreviation) or pronouncing it as a new word (acronym). In Spanish, there is a high relationship between letters and phonemes. Because of this, when a CLS is similar to other words in Spanish, there is a high tendency to pronounce it as a standard word. This paper proposes an automatic method for detecting acronyms. Additionaly, this paper analyses the discrimination capability of some features, and several strategies for combining them in order to obtain the best classifier. For the best classifier, the classification error is 8.45%. About the feature analysis, the best features have been the Letter Sequence Perplexity and the Average N-gram order

The Simple4All entry to the Blizzard Challenge 2013

We describe the synthetic voices entered into the 2013 Blizzard Challenge by the SIMPLE4ALL consortium. The 2013 Blizzard Challenge presents an opportunity to test and benchmark some of the tools we have been developing to address two problems of interest: 1) how best to learn from plentiful ‘found’ data, and 2) how to produce systems in arbitrary new languages with minimal annotated data and language-specific expertise on the part of the system builders. We here explain how our tools were used to address these problems on the different tasks of the challenge, and provide some discussion of the evaluation results. Index Terms: statistical parametric speech synthesis, speech alignment, speech segmentation, style diarisation, unsupervise