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Abstract: It is a well-known fact that the degree distribution (DD) of the nodes in a partition of a bipartite network influences the DD of its one-mode projection on that partition. However, there are no studies exploring the effect of the DD of the other partition on the one-mode projection. In this article, we show that the DD of the other partition, in fact, has a very strong influence on the DD of the one-mode projection. We establish this fact by deriving the exact or approximate closed-forms of the DD of the one-mode projection through the application of generating function formalism followed by the method of iterative convolution. The results are cross-validated through appropriate simulations. [Copyright &y& Elsevier]

In traditional generative linguistics sounds of a language are represented as bundle of binary valued features. The sounds used in a language are not randomly chosen from a universal repository of phonemes, but are known to be correlated in terms of the features they use. Discovery of these correlation patterns and organizational principles behind the structure of sound inventories has been one of the classic problems in phonology. In this work, we show that the amount of redundancy present in the sound inventory of a language, which is an information theoretic measure reflecting the ratio of the number of distinctive features used in the language to that of the minimum number of features required to distinguish between the sounds present in the language, lies within a very narrow range irrespective of the factors such as the size of the inventory, the language family and the typology. This is a hitherto unreported significant observation that points to a universal structural property of the sound inventories of human languages. This property might be an outcome of self-organization of the sound inventories through the processes of language acquisition and change, or of the way in which phonemes are represented in generative phonology. [ABSTRACT FROM AUTHOR]

The sound inventories of the world's languages self-organize themselves, giving rise to similar cross-linguistic patterns. In this work, we attempt to capture this phenomenon of self-organization, which shapes the structure of the consonant inventories, through a complex network approach. For this purpose we define the occurrence and co-occurrence networks of consonants and systematically study some of their important topological properties. A crucial observation is that the occurrence as well as the co-occurrence of consonants across languages follows a power law distribution. This property is arguably a consequence of the principle of preferential attachment. In order to support this argument we propose a synthesis model, which reproduces the degree distribution for the networks to a close approximation. We further observe that the co-occurrence network of consonants shows a high degree of clustering and subsequently refine our synthesis model in order to explain this property. Finally, we discuss how preferential attachment manifests itself through the evolutionary nature of language. [ABSTRACT FROM AUTHOR]

In this work, we attempt to capture patterns of co-occurrence across vowel systems and at the same time figure out the nature of the force leading to the emergence of such patterns. For this purpose, we define a weighted network where the vowels are the nodes and an edge between two nodes (read vowels) signifies their co-occurrence likelihood over the vowel inventories. Through this network we identify communities of vowels, which essentially reflect their patterns of co-occurrence across languages. We observe that in the communities formed by the frequently occurring vowels, the constituent nodes are largely uncorrelated in terms of their features and show that they are formed based on the principle of maximal perceptual contrast. However, in the rest of the communities, strong correlations are reflected among the constituent vowels with respect to their features, indicating that it is the principle of feature economy that binds them together. We validate the above observations by proposing a quantitative measure of perceptual contrast as well as feature economy and subsequently comparing the results obtained due to these quantifications with those where we assume that the vowel inventories had evolved just by chance. [ABSTRACT FROM AUTHOR]

Speech sounds of the languages all over the world show remarkable patterns of co-occurrence. In this work, we attempt to automatically capture the patterns of co-occurrence of the consonants across languages and at the same time figure out the nature of the force leading to the emergence of such patterns. For this purpose we define a weighted network where the consonants are the nodes and an edge between two nodes (read consonants) signify their co-occurrence likelihood over the consonant inventories. Through this network we identify communities of consonants that essentially reflect their patterns of co-occurrence across languages. We test the goodness of the communities and observe that the constituent consonants frequently occur in such groups in real languages also. Interestingly, the consonants forming these communities reflect strong correlations in terms of their features, which indicate that the principle of feature economy acts as a driving force towards community formation. In order to measure the strength of this force we propose a theoretical information definition of feature economy and show that indeed the feature economy exhibited by the consonant communities are substantially better than that of those where the consonant inventories had evolved just by chance. [ABSTRACT FROM AUTHOR]

Getting maximum battery life is not just a matter of writing applications for low average power consumption. You can take advantage of power effects to design systems that last longer on one charge. This work has shown that the performance of the text-to-speech engine can be optimized by judicious choice over factors such as word-cluster length. The approach demonstrated using this text-to-speech system can be easily extended to other applications for low-power, mobile embedded systems. [ABSTRACT FROM AUTHOR]

Recently, there has been a revival of interest in multi-agent simulation techniques for exploring the nature of language change. However, a lack of appropriate validation of simulation experiments against real language data often calls into question the general applicability of these methods in modeling realistic language change. We try to address this issue here by making an attempt to model the phenomenon of schwa deletion in Hindi through a multi-agent simulation framework. The pattern of Hindi schwa deletion and its diachronic nature are well studied, not only out of general linguistic inquiry, but also to facilitate Hindi grapheme-to-phoneme conversion, which is a preprocessing step to text-to-speech synthesis. We show that under certain conditions, the schwa deletion pattern observed in modern Hindi emerges in the system from an initial state of no deletion. The simulation framework described in this work can be extended to model other phonological changes as well. [ABSTRACT FROM AUTHOR]

Genes and human languages are discrete combinatorial systems (DCSs), in which the basic building blocks are finite sets of elementary units: nucleotides or codons in a DNA sequence, and letters or words in a language. Different combinations of these finite units give rise to potentially infinite numbers of genes or sentences. This type of DCSs can be represented as an alphabetic bipartite network (ABN) where there are two kinds of nodes, one type represents the elementary units while the other type represents their combinations. Here, we extend and generalize recent analytical findings for ABNs derived in [Peruani, Europhys. Lett. 79, 28001 (2007)] and empirically investigate two real world systems in terms of ABNs, the codon gene and the phoneme-language network. The one-mode projections onto the elementary basic units are also studied theoretically as well as in real world ABNs. We propose the use of ABNs as a means for inferring the mechanisms underlying the growth of real world DCSs.