That word doesn’t exist in my language: teaching and learning Laban/Bartenieff Movement System in multiple languages

The remarkable human capacity for bilingual and multilingual acquisition raises fundamental questions about how the brain develops efficient systems for processing multiple languages. In this study, we used neural network models trained on natural speech input to examine how these efficient representations emerge. Our models show that multiple phonological systems can be organized through parallel representations, preserving the unique aspects of each language while maintaining shared articulatory features. This parallel structure scaled effectively from two to three languages without needing additional neural architecture, highlighting the inherent efficiency in multilingual processing. Furthermore, the development of phonological representations varied based on the timing of language exposure, showing how earlier-learned languages shape the acquisition of subsequent ones. These findings imply that the human ability to speak multiple languages may arise from general principles of neural organization that optimize shared resources while maintaining essential distinctions between languages. This work has important implications for language learning, brain plasticity, and cognitive development.

We report on a half-semester course focused around implementation of type systems in programming languages. The course assumes basics of classical compiler construction, in particular, the abstract syntax representation, the Visitor pattern, and parsing. The course is built around a language Stella with a minimalistic core and a set of small extensions, covering algebraic data types, references, exceptions, exhaustive pattern matching, subtyping, recursive types, universal polymorphism, and type reconstruction. Optionally, an implementation of an interpreter and a compiler is offered to the students. To facilitate fast development and variety of implementation languages we rely on the BNF Converter tool and provide templates for the students in multiple languages. Finally, we report some results of teaching based on students' achievements.

A text-to-speech synthesis system is expected to convert any given text to highly intelligible and natural speech that sounds as human-like as possible. It finds its place in a variety of applications, like virtual assistants, speech-assistive aids, and speech-to-speech translation systems. In recent years several deep learning-based methods have been proposed for text-to-speech synthesis, which result in synthetic speech that is intelligible and very natural. However, these methods require a large amount of speech data, in the order of tens of hours, preferably recorded from a single speaker and in a quiet/studio environment. While such a large amount of data might be easily available for certain languages, like English, which are widely spoken and used globally, this is not the case with several other languages. In this regard, the current work explores state-of-the-art text-to-speech synthesis methods and strategies for handling low-resource languages. The paper ultimately aims to provide insight into developing text-to-speech systems for Indian languages and hence reviews the work done so far on Indian languages as well. Since India is a multilingual country where several languages co-exist, a multilingual synthesizer would be preferable. Further, one recurring suggestion to handle data scarcity is to make use of data from multiple languages too. Therefore, the current work focuses on exploring multilingual text-to-speech synthesis for low-resource languages.

This paper describes challenges and solutions for building a successful voice search system as applied to Japanese and Korean at Google. We describe the techniques used to deal with an infinite vocabulary, how modeling completely in the written domain for language model and dictionary can avoid some system complexity, and how we built dictionaries, language and acoustic models in this framework. We show how to deal with the difficulty of scoring results for multiple script languages because of ambiguities. The development of voice search for these languages led to a significant simplification of the original process to build a system for any new language which in in parts became our default process for internationalization of voice search.

A layered architecture that can provide a unified environment for multiparadigm programming on sequential processors is described. The architecture gives open-endedness to a system for programming in multiple languages based on multiple paradigms. Language-specific constructs such as control structures and variable definitions are given in an upper layer. Lower layers describe concepts common to all languages, such as data types and reference protocols. Since communication among languages is handled in the lower layers, the upper layers provide an open-ended abstraction for multiparadigm programming. >

Being bilingual has consequences for lexical retrieval in either language, in some situations leading to reduced access to one of a bilingual’s languages—suggesting the possibility of language attrition. In this chapter, we consider how a general memory mechanism behind forgetting can inform the discussion of bilingual language control. In particular, we focus on retrieval induced forgetting (RIF), which is believed to be driven by inhibition. Research into the psycholinguistic mechanisms supporting bilingual language processing has similarly accumulated a growing body of evidence that inhibitory mechanisms play an important role in developing and maintaining proficiency in multiple languages. We consider whether the phenomenon of native language attrition in fact reflects language loss per se or instead reflects the dynamics of a highly-interactive language system that is constantly in flux.

Differential neural representations of syntactic and semantic information across languages in Chinese-English bilinguals

The study of how speech is produced, transmitted, and perceived is a critical component in the curriculum of multiple disciplines—linguistics, communication science and disorders, cognitive science, and speech technology all rely on a fundamental understanding of speech science. Pedagogy in speech science across these disciplines has a rich history of experiential learning techniques. Despite being at the forefront of pedagogical innovations, speech science courses have lagged in terms of their representation of cultural and linguistic diversity in the classroom. Many speech scientists understand that linguistic diversity is a part of all human language systems. However, in our experience, relatively few courses involve the purposeful inclusion of multiple language varieties throughout the course across all topics. The goal of this paper is to highlight how to be more inclusive in teaching speech science.