Small Vocabulary Research Articles

Infant-directed communication: Examining the many dimensions of everyday caregiver-infant interactions.

Everyday caregiver-infant interactions are dynamic and multidimensional. However, existing research underestimates the dimensionality of infants' experiences, often focusing on one or two communicative signals (e.g., speech alone, or speech and gesture together). Here, we introduce "infant-directed communication" (IDC): the suite of communicative signals from caregivers to infants including speech, action, gesture, emotion, and touch. We recorded 10min of at-home play between 44 caregivers and their 18- to 24-month-old infants from predominantly white, middle-class, English-speaking families in the United States. Interactions were coded for five dimensions of IDC as well as infants' gestures and vocalizations. Most caregivers used all five dimensions of IDC throughout the interaction, and these dimensions frequently overlapped. For example, over 60% of the speech that infants heard was accompanied by one or more non-verbal communicative cues. However, we saw marked variation across caregivers in their use of IDC, likely reflecting tailored communication to the behaviors and abilities of their infant. Moreover, caregivers systematically increased the dimensionality of IDC, using more overlapping cues in response to infant gestures and vocalizations, and more IDC with infants who had smaller vocabularies. Understanding how and when caregivers use all five signals-together and separately-in interactions with infants has the potential to redefine how developmental scientists conceive of infants' communicative environments, and enhance our understanding of the relations between caregiver input and early learning. RESEARCH HIGHLIGHTS: Infants' everyday interactions with caregivers are dynamic and multimodal, but existing research has underestimated the multidimensionality (i.e., the diversity of simultaneously occurring communicative cues) inherent in infant-directed communication. Over 60% of the speech that infants encounter during at-home, free play interactions overlap with one or more of a variety of non-speech communicative cues. The multidimensionality of caregivers' communicative cues increases in response to infants' gestures and vocalizations, providing new information about how infants' own behaviors shape their input. These findings emphasize the importance of understanding how caregivers use a diverse set of communicative behaviors-both separately and together-during everyday interactions with infants.

Interpretable Compositional Representations for Robust Few-Shot Generalization.

We propose Recognition as Part Composition (RPC), an image encoding approach inspired by human cognition. It is based on the cognitive theory that humans recognize complex objects by components, and that they build a small compact vocabulary of concepts to represent each instance with. RPC encodes images by first decomposing them into salient parts, and then encoding each part as a mixture of a small number of prototypes, each representing a certain concept. We find that this type of learning inspired by human cognition can overcome hurdles faced by deep convolutional networks in low-shot generalization tasks, like zero-shot learning, few-shot learning and unsupervised domain adaptation. Furthermore, we find a classifier using an RPC image encoder is fairly robust to adversarial attacks, that deep neural networks are known to be prone to. Given that our image encoding principle is based on human cognition, one would expect the encodings to be interpretable by humans, which we find to be the case via crowd-sourcing experiments. Finally, we propose an application of these interpretable encodings in the form of generating synthetic attribute annotations for evaluating zero-shot learning methods on new datasets.

Effectiveness of Increasing Vocabulary and Constructing Sentences for High Class 4/5/6 SDLB for the Deaf Using the MMR Method with Video Media at SLB Riverkids and SLB Putra Jaya

Deafness is a condition where a child experiences problems with his ability to hear. Deaf children have a small vocabulary. The small vocabulary is caused by poor listening skills so that children are unable to accept new vocabulary which results in difficulties in communicating. Apart from difficulties in communication, they also have difficulty in constructing sentences. One step that can overcome the above problem is to apply the Maternal Reflective Method (MMR). The Reflective Maternal Method (MMR) emphasizes the mother's way of teaching language to children, where the teaching is carried out scientifically. The aim of this research is to analyze MMR on the ability of deaf children in adding vocabulary and constructing sentences. This type of research is Quasi Experimental research with a one group pretest posttest design, which applies five pretests and five posttests. Analysis technique for testing hypotheses using the SPSS 25 for Windows program.The research results showed a significant difference between the pretest results and the posttest results. This is proven by the output results obtained p is 0.000, which means p<0.05 then H0 is rejected and Ha is accepted. This means that there is a significant increase in the vocabulary and sentence learning outcomes of deaf students.The conclusion of this research is that the Maternal Reflective Method (MMR) can influence the addition of vocabulary and sentence construction in deaf children

Using TikTok as a Tool for English Vocabulary Learning in the EFL Context

One of the most crucial components of studying a language is learning its vocabulary. Without sufficient vocabulary, communication becomes problematic. However, research shows that Saudi Arabian university students have a small English vocabulary size. Many technological tools exist in the literature for increasing students’ vocabulary size, including TikTok videos. TikTok has become one of the most popular social media platforms for producing and sharing videos. Because students’ perception of tools used in the classroom determine the tools’ success, investigating their perception of using TikTok videos for English vocabulary learning is essential. In this study, a quantitative research design was used to investigate students’ perception toward using TikTok as an English vocabulary learning tool in the EFL context. A descriptive quantitative methodology was adopted to analyze data from 115 female students at King Abdulaziz University. According to the findings, most students have a positive perception toward the use of TikTok videos as a vocabulary learning tool. These findings suggest that TikTok videos can be used to increase students’ English vocabulary size. The current study proposes an alternative pedagogical means for teaching English vocabulary to language teachers who want to help their students expand and develop their vocabulary.

SignRing

Sign language is a natural language widely used by Deaf and hard of hearing (DHH) individuals. Advanced wearables are developed to recognize sign language automatically. However, they are limited by the lack of labeled data, which leads to a small vocabulary and unsatisfactory performance even though laborious efforts are put into data collection. Here we propose SignRing, an IMU-based system that breaks through the traditional data augmentation method, makes use of online videos to generate the virtual IMU (v-IMU) data, and pushes the boundary of wearable-based systems by reaching the vocabulary size of 934 with sentences up to 16 glosses. The v-IMU data is generated by reconstructing 3D hand movements from two-view videos and calculating 3-axis acceleration data, by which we are able to achieve a word error rate (WER) of 6.3% with a mix of half v-IMU and half IMU training data (2339 samples for each), and a WER of 14.7% with 100% v-IMU training data (6048 samples), compared with the baseline performance of the 8.3% WER (trained with 2339 samples of IMU data). We have conducted comparisons between v-IMU and IMU data to demonstrate the reliability and generalizability of the v-IMU data. This interdisciplinary work covers various areas such as wearable sensor development, computer vision techniques, deep learning, and linguistics, which can provide valuable insights to researchers with similar research objectives.

Receptive Vocabulary Size of Omani Post-Basic Education Graduates

This descriptive study examined the receptive vocabulary size of Omani post-basic education graduates. Also, it investigated the relationship between receptive vocabulary size and language proficiency among these students. Finally, it examined differences in receptive vocabulary size with respect to gender. The sample consisted of 294 Omani post-basic education graduates (183 males and 111 females) who were enrolled in foundation programs at Sultan Qaboos University. The study instrument was the Vocabulary Level Test Version B, which was developed by Nation (1983) and revised by Schmitt, Schmitt, and Clapham (2001). The study found that the Omani post-basic graduates had a small receptive vocabulary of approximately 1,725 word families. It also found a statistically significant relationship between students’ receptive vocabulary size and their language proficiency. Finally, it revealed statistically significant differences in receptive vocabulary size with respect to gender in favor of female students. Accordingly, the researcher provides recommendations for practice and further research.

Vocabulary Change in Process Writing: Effect of Text Structure Instruction

Abstract The purpose of this study, conducted as a follow-up study of Oshima (2020), was to examine whether Japanese EFL students’ use of vocabulary changed after being given lessons on explicit instruction on text structure and process writing. Two groups of college students—the beginner-level group writing a descriptive essay and the advanced-level group writing an argumentative essay—wrote an outline, the first draft (D1), the second draft (D2), and the final draft (FD), and I examined the differences in lexical richness between students’ D1 and FD with New Word Level Checker (Mizumoto, 2021). The results showed that both groups’ drafts had changed in the number of words used (tokens), the number of unique words used (types), and the number of lower frequency words used. This study’s finding also supports the importance of choosing an appropriate measurement to analyze students’ vocabulary levels. For Japanese students, 1K-word bands, which have been widely used in previous literature, seem too broad to capture their small vocabulary improvement.

Twenty-month-olds categorically discriminate similar sounding vowels regardless of vocabulary level, an event related potentials (ERP) study.

The current study investigated whether vocabulary relates to phonetic categorization at neural level in early childhood. Electoencephalogram (EEG) responses were collected from 53 Dutch 20-month-old children in a passive oddball paradigm, in which they were presented with two nonwords "giep" [ɣip] and "gip" [ɣɪp] that were contrasted solely by the vowel. In the multiple-speaker condition, both nonwords were produced by twelve different speakers; while, in the single-speaker condition, one single token of each word was used as stimuli. Infant positive mismatch responses (p-MMR) were elicited in both conditions without significant amplitude differences. When the infants were median split based on vocabulary level, the large and small vocabulary groups showed comparable p-MMR amplitudes yet different scalp distribution in both conditions. These results suggest successful phonetic categorization of native similar sounding vowels at 20 months, and a close relationship between speech categorization and vocabulary development.

Frequent vs. infrequent words shape toddlers' real-time sentence comprehension.

We examined how noun frequency and the typicality of surrounding linguistic context contribute to children's real-time comprehension. Monolingual English-learning toddlers viewed pairs of pictures while hearing sentences with typical or atypical sentence frames (Look at the… vs. Examine the…), followed by nouns that were higher- or lower-frequency labels for a referent (horse vs. pony). Toddlers showed no significant differences in comprehension of nouns in typical and atypical sentence frames. However, they were less accurate in recognizing lower-frequency nouns, particularly among toddlers with smaller vocabularies. We conclude that toddlers can recognize nouns in diverse sentence contexts, but their representations develop gradually.

What do children’s errors tell us about the strategies used during a word inferencing task?

A vital and often overlooked aspect of word learning is the ability to establish deep semantic knowledge by adjusting and fine-tuning new word meanings as information becomes available. Here we studied differences in children’s ability to update incorrect or incomplete word meanings by studying error types in a word inferencing task. The participants, 45 8- and 9-year-olds, read three sentences that all ended with the same nonsense word and were asked to identify the meaning of the last word. Importantly, the third sentence always provided the most useful information about the word’s meaning. When children made errors, two types of responses were of interest. The first was when children gave a response that ignored the third sentence but fit one or two of the earlier ones. This suggests that the children had failed to update the meaning accurately. The second was when children were given enough information in the three sentences yet said that they were still unable to identify a word meaning. This suggests that the children would not attempt to infer a word when they were unsure of the answer. When controlling for number of correct responses, we found that children with smaller vocabularies were significantly more likely to fail to incorporate the third sentence, whereas children with large vocabularies were more likely to say that they were still unable to identify a meaning. These findings indicate that children with smaller vocabularies may be at risk of incorrectly inferring a new word’s meaning rather than seeking further information to ensure accuracy.

Audio to Sign Language Translator

Abstract: This project is based on converting the audio signals receiver to text using speech to text API. Speech to text conversion comprises of small, medium and large vocabulary conversions. Such systems process or accept the voice which then gets converted to their respective text. This paper gives a comparative analysis of the technologies used in small, medium, and large vocabulary Speech Recognition System. The comparative study determines the benefits and liabilities of all the approaches so far. The experiment shows the role of language models in improving the accuracy of speech to text conversion system. We experiment the speech data with noisy sentences and incomplete words. The results show a prominent result for randomly chosen sentences compared to sequential set of sentences

Small vocabulary isolated-word automatic speech recognition for single-word commands in Arabic spoken

Small vocabulary isolated-word automatic speech recognition for single-word commands in Arabic spoken

Different lexicons make different rivals

Analogy-based theories assume that in situations of affix competition, language users create novel word forms on the basis of similar existing forms in their Mental Lexicons ( Baayen et al. 2011 ; Daelemans & van den Bosch 2005; Skousen 1989 ). Interestingly, however, simulation studies employing computational implementations of analogical theories have almost invariably adopted a rather abstractionist view of the Mental Lexicon, representing the word stock of the language, and abstracting away from differences between individual speakers (see, e.g., Arndt-Lappe 2014 ; Chapman & Skousen 2005 ; Eddington 2006 ; Nieder et al. 2021 ). This is a problem because it precludes the possibility of testing a central prediction of analogical theories: if affixes are assigned on the fly on the basis of similar words in the lexicon, then speakers with different lexicons should make different choices. The present paper provides a proof-of-concept study addressing this issue for the form-based rivalry between the two English adjectival suffixes - ic and - ical. Analogical Modeling of Language (AML; Skousen et al. 2013 ) is used as a computational model. On the basis of a survey of the distribution of derivatives in different registers in the British National Corpus, predictions of the analogical model are compared for a simulated speaker with a large vocabulary (including academic words) and a simulated speaker with a small vocabulary that is based mainly on words from spoken language. The statistical analysis of the simulations reveals that, while sharing some basic properties, the two models make very clear – and testable – predictions about speaker differences.

ORYOL AND KOSTROMA RESEARCHERS OF RUSSIAN FOLKISH WORD: ORYOL-KOSTROMA TIES

The new works of dialectologists from Turgenev Oryol State University – issues of the large-scale alphabetical Dictionary of Oryol patois and small thematic vocabularies, monographs, scientific articles etc – are analysed in the article. The topic of the long-standing scientific and cultural ties between Kostroma and Oryol dialectologists, in the form of presentations at conferences held at Kostroma State University in recent years, is addressed as well. The next 2 volumes of the “Dictionary of Oryol patoisˮ – vol. 16, letters У to Ф, and vol. 17, letters Х to Ц [Dictionaries of Oryol patois-16 and -17] are in comparative perspective with the similar dictionary collections in this and other Russian regions in number of entries, composition and source of words, in peculiarities of lexical semantisation. The contribution of their compilers, the scientific editor of the Dictionary's editions, as well as their collaborators and colleagues in the study of Oryol folkish patois is well defined; their reliance on the works of the preceding generations of Oryol linguists is addressed as well. The work of those representatives of Oryol dialectological school under the guidance of Tat'yana Vasil'yevna Bakhvalova, who strengthened the scientific ties, both thematic and subject, with Kostroma dialectologists through the participation in joint research projects and scientific meetings, mainly connected with the systematic holding the readings in memory of Aleksandr Gromov at Kostroma State University, is noted. The conclusion about the fruitfulness of the forms of cooperation between Oryol and Kostroma dialectologists is made.

Development of Small Vocabulary Continuous Speech-to-Text System for Kannada Language/Dialects

Objectives: To develop a speech-to-text (STT) system using Kaldi speech recognition toolkit for continuous Kannada language/dialects. Methods: A continuous Kannada speech data is collected from 100 speakers/farmers of Karnataka state in field. The lexicon/dictionary and set of phonemes for Kannada language/dialects are created and transcribed the collected speech data using transcriber tool. The ASR models are developed at different phoneme levels using Kaldi. Findings: In this work, an effort is made to develop a robust small vocabulary STT system for continuous Kannada language using Kaldi. The various acoustic modelling techniques are used to develop a robust ASR model and achieved a word error rate (WER) of 0.23%. The performance of the developed ASR model is compared with existing works and analyzed by offline speech recognition. Novelty: Many STT systems have been developed for Indian and International languages/dialects, but not for Kannada language. This work is first of its kind using Kaldi in Kannada language under the constraints of limited data. The developed ASR model could be used further in the development of end-to-end ASR system for speech processing applications. Keywords: Automatic Speech Recognition (ASR); Word Error Rate (WER); Continuous Kannada Speech Data; Kannada Language/Dialects; Lexicon

Borderless Fusion Financial Management Innovation Based on Speech Recognition Technology

In recent years, the rapid development of information technology has affected the operation of the world economy, and the emergence of e-commerce has decreased significantly. The development of modern information technology, especially the development of Internet technology, provides a solid foundation for e-commerce. The time and space gap between economic operators is shortened, and resources are shared as widely as possible. Based on a comprehensive and in-depth study of language recognition technology. However, the current e-commerce financial management still uses the traditional accounting method, which is inefficient and cannot be integrated with new technologies. This paper proposes a design scheme for a nonspecific person and small vocabulary language recognition system based on SPCE061A processor. This integrated system can be combined with data acquisition and language input technology and constitute a data acquisition and input system. It is used for data collection and input of instruments and meters in the workplace to improve work efficiency. This paper proposes algorithms based on time-domain energy and zero-crossing rate detection, detection based on entropy function method, and detection based on the double-threshold method. The speech recognition technology is analyzed in detail, and a new model of financial management is constructed. Accounting information disclosure is a virtuous circle of “promoting capital management with appropriate decentralization.” The experimental results in this paper show that through model construction, the calculated WER is 0 dm, 7 dm, 15 dm, and 11 dm, respectively, and it is concluded that the system exhibits good antinoise performance.

Reliability-Based Large-Vocabulary Audio-Visual Speech Recognition.

Audio-visual speech recognition (AVSR) can significantly improve performance over audio-only recognition for small or medium vocabularies. However, current AVSR, whether hybrid or end-to-end (E2E), still does not appear to make optimal use of this secondary information stream as the performance is still clearly diminished in noisy conditions for large-vocabulary systems. We, therefore, propose a new fusion architecture—the decision fusion net (DFN). A broad range of time-variant reliability measures are used as an auxiliary input to improve performance. The DFN is used in both hybrid and E2E models. Our experiments on two large-vocabulary datasets, the Lip Reading Sentences 2 and 3 (LRS2 and LRS3) corpora, show highly significant improvements in performance over previous AVSR systems for large-vocabulary datasets. The hybrid model with the proposed DFN integration component even outperforms oracle dynamic stream-weighting, which is considered to be the theoretical upper bound for conventional dynamic stream-weighting approaches. Compared to the hybrid audio-only model, the proposed DFN achieves a relative word-error-rate reduction of 51% on average, while the E2E-DFN model, with its more competitive audio-only baseline system, achieves a relative word error rate reduction of 43%, both showing the efficacy of our proposed fusion architecture.

Estimating Sentence-like Structure in Synthetic Languages Using Information Topology.

Estimating sentence-like units and sentence boundaries in human language is an important task in the context of natural language understanding. While this topic has been considered using a range of techniques, including rule-based approaches and supervised and unsupervised algorithms, a common aspect of these methods is that they inherently rely on a priori knowledge of human language in one form or another. Recently we have been exploring synthetic languages based on the concept of modeling behaviors using emergent languages. These synthetic languages are characterized by a small alphabet and limited vocabulary and grammatical structure. A particular challenge for synthetic languages is that there is generally no a priori language model available, which limits the use of many natural language processing methods. In this paper, we are interested in exploring how it may be possible to discover natural ‘chunks’ in synthetic language sequences in terms of sentence-like units. The problem is how to do this with no linguistic or semantic language model. Our approach is to consider the problem from the perspective of information theory. We extend the basis of information geometry and propose a new concept, which we term information topology, to model the incremental flow of information in natural sequences. We introduce an information topology view of the incremental information and incremental tangent angle of the Wasserstein-1 distance of the probabilistic symbolic language input. It is not suggested as a fully viable alternative for sentence boundary detection per se but provides a new conceptual method for estimating the structure and natural limits of information flow in language sequences but without any semantic knowledge. We consider relevant existing performance metrics such as the F-measure and indicate limitations, leading to the introduction of a new information-theoretic global performance based on modeled distributions. Although the methodology is not proposed for human language sentence detection, we provide some examples using human language corpora where potentially useful results are shown. The proposed model shows potential advantages for overcoming difficulties due to the disambiguation of complex language and potential improvements for human language methods.

Vocabulary and reading speed in the majority language are affected by maternal language proficiency and language exposure at home: a study of language minority bilingual children in Italy

ABSTRACT Environmental sources of variance in the Italian vocabulary and reading skills of bilingual primary school children from immigrant families (or language minority bilingual children (LMBC)) in Italy were investigated, and the LMBC’s skill levels were compared to those of their monolingual classmates. A total of 140 children from the first, third, and fifth grades were administered standardized tests of receptive vocabulary, word reading, and nonword reading skills. LMBC’s exposure to Italian at home and their mothers’ Italian proficiency were reported by mothers. Immigrant mothers’ Italian proficiency was a significant predictor of vocabulary and word reading speed but not of word accuracy or nonword reading. High levels of Italian exposure at home in combination with low levels of maternal Italian proficiency was a negative predictor of vocabulary; high exposure to Italian at home was positively associated with word reading speed and negatively associated with nonword reading speed. LMBC had lower vocabulary scores but did not differ from monolingual children in measures of reading skill. The findings underscore the importance of the quality of language exposure for language development and replicate findings that bilingual children show decoding skills on a par with monolingual children despite smaller vocabularies.

The Vocabulary of Infants with an Elevated Likelihood and Diagnosis of Autism Spectrum Disorder: A Systematic Review and Meta-Analysis of Infant Language Studies Using the CDI and MSEL.

Diagnoses of autism spectrum disorder (ASD) are typically accompanied by atypical language development, which can be noticeable even before diagnosis. The siblings of children diagnosed with ASD are at elevated likelihood for ASD diagnosis and have been shown to have higher prevalence rates than the general population. In this paper, we systematically reviewed studies looking at the vocabulary size and development of infants with autism. One inclusion criterion was that infants were grouped either pre-diagnostically as elevated or typical likelihood or post-diagnostically as ASD or without ASD. This review focused on studies that tested infants up to 24 months of age and that assessed vocabulary either via the parent-completed MacArthur–Bates Communicative Developmental Inventory (CDI) or the clinician-administered Mullen Scales of Early Learning (MSEL). Our systematic search yielded 76 studies. A meta-analysis was performed on these studies that compared the vocabulary scores of EL and TL infants pre-diagnostically and the scores of ASD and non-ASD infants post-diagnostically. Both pre- and post-diagnostically, it was found that the EL and ASD infants had smaller vocabularies than their TL and non-ASD peers, respectively. The effect sizes across studies were heterogenous, prompting additional moderator analyses of age and sub-group analyses of the language measure used (CDI or MSEL) as potential moderators of the effect size. Age was found to be a moderator both in the pre- and post-diagnostical groups, however, language measure was not a moderator in either diagnostic group. Interpretations and future research directions are discussed based on these findings.