A bilingual Spanish-Catalan database of units for concatenative synthesis

Different databases of phonetic units are required in multilingual Text-to-Speech systems based on concatenative synthesis. We are currently developing a TTS system able to convert text either in Catalan and Spanish, with some of the modules being used indistinctly by the two languages while others are specific to each language. In order to reduce the total amount of units, a bilingual database has been obtained from two monolingual databases recorded by the same speaker, which contains all possible units for both languages. Common units have been selected according to their phonetic representation. The bilingual database has 1099 units, including diphones and some long units, while the two monolingual databases would result in 1545 units. An analysis of Catalan unit frequencies has been done to select what units should be included in the database. The experiments carried out showed that that synthetic speech has a strong Catalan accent, probably due to the speaker's accent. Some common units, even if they are represented with the same symbol, should be considered separately in a bilingual database in order to cope with acoustically different allophones.Peer ReviewedPostprint (published version

Linguistic unit discovery from multi-modal inputs in unwritten languages: Summary of the "Speaking Rosetta" JSALT 2017 Workshop

We summarize the accomplishments of a multi-disciplinary workshop exploring the computational and scientific issues surrounding the discovery of linguistic units (subwords and words) in a language without orthography. We study the replacement of orthographic transcriptions by images and/or translated text in a well-resourced language to help unsupervised discovery from raw speech.Comment: Accepted to ICASSP 201

Introduction to the special issue on cross-language algorithms and applications

With the increasingly global nature of our everyday interactions, the need for multilingual technologies to support efficient and efective information access and communication cannot be overemphasized. Computational modeling of language has been the focus of Natural Language Processing, a subdiscipline of Artificial Intelligence. One of the current challenges for this discipline is to design methodologies and algorithms that are cross-language in order to create multilingual technologies rapidly. The goal of this JAIR special issue on Cross-Language Algorithms and Applications (CLAA) is to present leading research in this area, with emphasis on developing unifying themes that could lead to the development of the science of multi- and cross-lingualism. In this introduction, we provide the reader with the motivation for this special issue and summarize the contributions of the papers that have been included. The selected papers cover a broad range of cross-lingual technologies including machine translation, domain and language adaptation for sentiment analysis, cross-language lexical resources, dependency parsing, information retrieval and knowledge representation. We anticipate that this special issue will serve as an invaluable resource for researchers interested in topics of cross-lingual natural language processing.Postprint (published version