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A multilingual synthesizer synthesizes speech, for any given monolingual or mixed-language text, that is intelligible to human listeners. The necessity for such synthesizer arises in a country like India, where multiple languages coexist. For the current work, multilingual synthesizers are developed using HMM-based speech synthesis technique. However, for a mixed-language text, the synthesized speech shows speaker switching at language switching points which is quite annoying to the listener. This is due to the fact that, speech data used for training is collected for each language from a different (native) speaker. To overcome the speaker switching at language switching points, a polyglot speech synthesizer is developed using polyglot speech corpus (all the speech data in a single speaker’s voice). The polyglot speech corpus is obtained using cross-lingual voice conversion (CLVC) technique. In the current work, polyglot synthesizer is developed for five languages namely Tamil, Telugu, Hindi, Malayalam and Indian English. The regional Indian languages considered are acoustically similar, to certain extent, and hence, common phoneset and question set is used to build the synthesizer. Experiments are carried out by developing various bilingual polyglot synthesizers to choose the language (thereby the speaker) that can be considered as target for polyglot synthesizer. The performance of the synthesizers is evaluated subjectively for speaker/language switching using perceptual test and quality using mean opinion score. Speaker identity is evaluated objectively using a GMM-based speaker identification system. Further, the polyglot synthesizer developed using polyglot speech corpus is compared with the adaptation-based polyglot synthesizer, in terms of quality of the synthesized speech and amount of data required for adaptation and voice conversion. It is observed that the performance of the polyglot synthesizer developed using polyglot speech corpus obtained from CLVC technique is better or almost similar to that of the adaptation-based polyglot synthesizer. [ABSTRACT FROM AUTHOR]

The view of sign languages as bounded systems is often important for deaf community empowerment and for pedagogical practice in terms of supporting deaf children’s language acquisition and second language learners’ communicative competence. Conversely, the notion of translanguaging in the American Sign Language (ASL) community highlights a number of recurring tensions related to standard language ideology and English-based sign systems in deaf education; these tensions can work to obscure the polyglot nature of ASL itself. This paper reports how translingual practices were embedded in an ethnographic action research study of developing and field-testing an ASL curriculum for parents of young deaf children that is aligned with theCommon European Framework of Reference for Languages(CEFR). In this study of teaching parents classical ASL varieties, translingual practices were highlighted where ASL and the visual code of English are regularly in contact with and influence each other in signers’ multimodal repertoires. This data illustrate the history of translanguaging in ASL communities and points to the importance of preserving historic deaf community language practices. [ABSTRACT FROM PUBLISHER]

Machine Translation (MT) requires a large amount of linguistic resources, which leads current MT systems to leaving unknownwords untranslated. This can be annoying for end users, as they might not understand at all such untranslated words. However, most language families share a common vocabulary, therefore this knowledge can be leveraged to produce more understandable translations, typically for "assimilation" or gisting use. Based on this observation, we propose a method that constructs polyglot translations tailored to a particular user language. Simply put, an unknown word is translated into a set of languages that relate to the user's language, and the translated word that is closest to the user's language is used as a replacement of the unknown word. Experimental results on language coverage over three language families indicate that our method may improve the usefulness of MT systems. As confirmed by a subsequent human evaluation, polyglot translations look indeed familiar to the users, and are perceived to be easier to read and understand than translations in their related natural languages. [ABSTRACT FROM AUTHOR]

Based on the salient features of the documents, automatic text summarization systems extract the key sentences from source documents. This process supports the users in evaluating the relevance of the extracted documents returned by information retrieval systems. Because of this tool, efficient filtering can be achieved. Indirectly, these systems help to resolve the problem of information overloading. Many automatic text summarization systems have been implemented for use with different languages. It has been established that the grammatical and lexical differences between languages have a significant effect on text processing. However, the impact of the language differences on the automatic text summarization systems has not yet been investigated. The authors provide an impact analysis of language difference on automatic text summarization. It includes the effect on the extraction processes, the scoring mechanisms, the performance, and the matching of the extracted sentences, using the parallel corpus in English and Chinese as the tested object. The analysis results provide a greater understanding of language differences and promote the future development of more advanced text summarization techniques. [ABSTRACT FROM AUTHOR]