Referential expressions in monolingual and bilingual children with and without Autism Spectrum Disorder (ASD): A study of informativeness and definiteness

The current study evaluated the separate and combined effects of bilingualism and Autism Spectrum Disorder (ASD) on informativeness and definiteness marking of referential expressions. Hebrew-speaking monolingual children (21 with ASD and 28 with typical language development) and Russian–Hebrew-speaking bilingual children (13 with ASD and 30 with typical language development) aged 4–9 years participated. Informativeness, indexed by referential contrasts, was affected by ASD, but not by bilingualism. Definiteness use was non-target-like in children with ASD and in bilingual children, and it was mainly predicted by children’s morpho-syntactic abilities in Hebrew. Language-universal and language-specific properties of referential use are discussed.

Gender Representation in the Lexical Functional Grammar

Gender marking is a language universal, although some languages have a stronger Gender-marking grammar. The Lexical Functional Grammar (LFG), a linguistic theory, has a set of rules and levels to render for Gender marking. Bornee and developed within the larger framework of the Generative Grammar, the Lexical Functional Grammar has become a standalone autonomous theoretical theory. This paper draws data from French language to present a comprehensive development of Gender-marking analysis within the Lexical Functional Grammar Framework. Fundamentally, the LSG posits for four phrase structures, which are the C-structure representing lexical entries, the F-structure, which deals with the functional information, the A-structure, which structures predicate-argument relationships, and the ơ-structure, which handles semantic representations. Although the grammatical gender is arrayed all over the four structures, it is mainly presented in this paper as a feature in the lexicon, typically integrated in the C-structure and F-structure mapping.

The Common Misconceptions about Sign Language

This paper entitled ‘The Common Misconceptions about Sign Language’ is concerned with the most common misconceptions about sign language. It also deals with sign language and its relation with the spoken language. Sign language, primarily used by deaf people, is a fully-developed human language that does not use sounds for communication, but it is a visual-gestural system that uses hands, body and facial gestures. One of the misconceptions is that all sign languages are the same in the worldwide. Such assumptions cause problems. Accordingly, some questions have been raised: first, is sign language universal? Second, is sign language based on spoken language? And third, is sign language invented by hearing people? The aim of the paper is to have a deeper understanding about sign language. It also demonstrates the similarities and differences between the two different modalities: sign language and spoken language. The paper is based on some hypothesis. One of the hypotheses is that sign languages are pantomimes and gestures. It also hypothesizes that the process of language acquisition in sign language for deaf people is different from the language acquisition in spoken language for hearing people. To answer the questions raised, the qualitative approach is adopted. The procedure is to collect data about the subject from books and articles and then analyze the data to obtain the aim of the study. One of the conclusions is that sign language is not universal. It is recommended that more work can be carried out on the differences between either American Sign Language (ASL) or British Sign Language (BSL) with reference to zmânî âmâžaî kurdî (ZAK) Kurdish Sign Language) at all linguistic levels.

Linguistic and Cultural Study of the Implicit Negation in Spanish

The article considers the category of negation in Spanish as one of the most important components of its culture. Being a language universal and a fundamental category of any culture, negation realizes a great variety of communicative functions. Their study leads to a better understanding of the communicative behavior of the foreign culture representatives. The article presents different points of view on the nature of negation, by analyzing and summarizing different approaches made by different scientists all over the world to the study of this linguistic phenomenon, thus highlighting two main aspects of investigation of negation: formal (syntactical) and conceptual (communicative). When functional capacity of the category in forming negative meanings within real communicative process is studied, the importance should be given to the cognitive and pragmatic features, irrespective of the set grammar rules of negation. In this connection, the article analyzes those forms of negation which are not expressed grammatically. The author has selected and analyzed a number of speech patterns, conveying negative meanings in the process of communication and the following groups were singled out among them: affirmative exclamation, rhetorical question, ironic response. The author stresses that in spite of being manifested at all levels of the language, the implicit negation does not have any fixed marker of expression and it’s always difficult to differentiate it in the context. However, its profound study will help to understand better the communicative behavior of the foreign-language speaking participants in the dialogical interaction. The article examines the comparative aspects of the negation study at the confluence of Russian and Spanish speaking cultures, spotting the differences in the linguistic world pictures.

Pronunciation augmentation for Mandarin-English code-switching speech recognition

Code-switching (CS) refers to the phenomenon of using more than one language in an utterance, and it presents great challenge to automatic speech recognition (ASR) due to the code-switching property in one utterance, the pronunciation variation phenomenon of the embedding language words and the heavy training data sparse problem. This paper focuses on the Mandarin-English CS ASR task. We aim at dealing with the pronunciation variation and alleviating the sparse problem of code-switches by using pronunciation augmentation methods. An English-to-Mandarin mix-language phone mapping approach is first proposed to obtain a language-universal CS lexicon. Based on this lexicon, an acoustic data-driven lexicon learning framework is further proposed to learn new pronunciations to cover the accents, mis-pronunciations, or pronunciation variations of those embedding English words. Experiments are performed on real CS ASR tasks. Effectiveness of the proposed methods are examined on all of the conventional, hybrid, and the recent end-to-end speech recognition systems. Experimental results show that both the learned phone mapping and augmented pronunciations can significantly improve the performance of code-switching speech recognition.

Left and right arcuate fasciculi are uniquely related to word reading skills in Chinese-English bilingual children

Whether reading in different writing systems recruits language-unique or language-universal neural processes is a long-standing debate. Many studies have shown the left Arcuate Fasciculus (AF) to be involved in phonological and reading processes. In contrast, little is known about the role of the right AF in reading, but some have suggested that it may play a role in visual spatial aspects of reading or the prosodic components of language. The right AF may be more important for reading in Chinese due to its logographic and tonal properties, but this hypothesis has yet to be tested. We recruited a group of Chinese-English bilingual children (8.2 to 12.0 years old) to explore the common and unique relation of reading skill in English and Chinese to Fractional Anisotropy (FA) in the bilateral AF. We found that both English and Chinese reading skills were positively correlated with FA in the rostral part of the left AF-direct segment. Additionally, English reading skill was positively correlated with FA in the caudal part of the left AF-direct segment, which was also positively correlated with phonological awareness. In contrast, Chinese reading skill was positively correlated with FA in certain segments of the right AF, which was positively correlated with visual spatial ability, but not tone discrimination ability. Our results suggest that there are language universal substrates of reading across languages, but that certain left AF nodes support phonological mechanisms important for reading in English, whereas certain right AF nodes support visual spatial mechanisms important for reading in Chinese.

Cascaded Convolutional Neural Network Architecture for Speech Emotion Recognition in Noisy Conditions

Convolutional neural networks (CNNs) are a state-of-the-art technique for speech emotion recognition. However, CNNs have mostly been applied to noise-free emotional speech data, and limited evidence is available for their applicability in emotional speech denoising. In this study, a cascaded denoising CNN (DnCNN)–CNN architecture is proposed to classify emotions from Korean and German speech in noisy conditions. The proposed architecture consists of two stages. In the first stage, the DnCNN exploits the concept of residual learning to perform denoising; in the second stage, the CNN performs the classification. The classification results for real datasets show that the DnCNN–CNN outperforms the baseline CNN in overall accuracy for both languages. For Korean speech, the DnCNN–CNN achieves an accuracy of 95.8%, whereas the accuracy of the CNN is marginally lower (93.6%). For German speech, the DnCNN–CNN has an overall accuracy of 59.3–76.6%, whereas the CNN has an overall accuracy of 39.4–58.1%. These results demonstrate the feasibility of applying the DnCNN with residual learning to speech denoising and the effectiveness of the CNN-based approach in speech emotion recognition. Our findings provide new insights into speech emotion recognition in adverse conditions and have implications for language-universal speech emotion recognition.

The Influence of Different Prosodic Cues on Word Segmentation

A prerequisite for spoken language learning is segmenting continuous speech into words. Amongst many possible cues to identify word boundaries, listeners can use both transitional probabilities between syllables and various prosodic cues. However, the relative importance of these cues remains unclear, and previous experiments have not directly compared the effects of contrasting multiple prosodic cues. We used artificial language learning experiments, where native German speaking participants extracted meaningless trisyllabic “words” from a continuous speech stream, to evaluate these factors. We compared a baseline condition (statistical cues only) to five test conditions, in which word-final syllables were either (a) followed by a pause, (b) lengthened, (c) shortened, (d) changed to a lower pitch, or (e) changed to a higher pitch. To evaluate robustness and generality we used three tasks varying in difficulty. Overall, pauses and final lengthening were perceived as converging with the statistical cues and facilitated speech segmentation, with pauses helping most. Final-syllable shortening hindered baseline speech segmentation, indicating that when cues conflict, prosodic cues can override statistical cues. Surprisingly, pitch cues had little effect, suggesting that duration may be more relevant for speech segmentation than pitch in our study context. We discuss our findings with regard to the contribution to speech segmentation of language-universal boundary cues vs. language-specific stress patterns.

Formal Basis of a Language Universal

Steedman (2020) proposes as a formal universal of natural language grammar that grammatical permutations of the kind that have given rise to transformational rules are limited to a class known to mathematicians and computer scientists as the “separable” permutations. This class of permutations is exactly the class that can be expressed in combinatory categorial grammars (CCGs). The excluded non-separable permutations do in fact seem to be absent in a number of studies of crosslinguistic variation in word order in nominal and verbal constructions. The number of permutations that are separable grows in the number n of lexical elements in the construction as the Large Schröder Number Sn−1. Because that number grows much more slowly than the n! number of all permutations, this generalization is also of considerable practical interest for computational applications such as parsing and machine translation. The present article examines the mathematical and computational origins of this restriction, and the reason it is exactly captured in CCG without the imposition of any further constraints.