Growing a SUPER BABY: An Investigation of Factors That Influence Toddlers Vocabulary Size

2006 ◽  
Author(s):  
Jillian McLellan ◽  
Casey A. Carroll ◽  
Brooke Soden Hensler ◽  
Peg Hull Smith ◽  
Wendelyn Shore
Keyword(s):  
Vocabulary Size

Stress assignment in bilingual adults reading Italian as a second language: The effect of vocabulary size

2011 ◽  
Author(s):  
C. Burani ◽  
S. Primativo ◽  
L. S. Arduino ◽  
S. O'Brien ◽  
D. Paizi ◽  
...  
Keyword(s):  
Second Language ◽  
Vocabulary Size ◽  
Stress Assignment

A Multivariate Approach to Lexical Diversity in Constrained Language

2020 ◽  
Vol 21 (2) ◽  
pp. 169-194
Author(s):  
Marta Kajzer-Wietrzny ◽  
Ilmari Ivaska
Keyword(s):  
Second Language Acquisition ◽  
Regression Models ◽  
Translation Studies ◽  
Methodological Framework ◽  
Multivariate Approach ◽  
Mediated Communication ◽  
Vocabulary Size ◽  
Lexical Diversity ◽  
Linear Mixed Effects ◽  
Word Repetition

Empirical Translation Studies have recently extended the scope of research to other forms of constrained and mediated communication, including bilingual communication, editing, and intralingual translation. Despite the diversity of factors accounted for so far, this new strand of research is yet to take the leap into intermodal comparisons. In this paper we look at Lexical Diversity (LD), which under different guises, has been studied both within Translation Studies (TS) and Second Language Acquisition (SLA). LD refers to the rate of word repetition, and vocabulary size and depth, and previous research indicates that translated and non-native language tends to be less lexically diverse. There is, however, no study that would investigate both varieties within a unified methodological framework. The study reported here looks at LD in spoken and written modes of constrained and non-constrained language. In a two-step analysis involving Exploratory Factor Analysis and linear mixed-effects regression models we find interpretations to be least lexically diverse and written non-constrained texts to be most diverse. Speeches delivered impromptu are less diverse than those read out loud and the non-constrained texts are more sensitive to such delivery-related differences than the constrained ones.

Vocabulary size and depth as predictors of second language speaking ability

System ◽  
2021 ◽  
Vol 99 ◽  
pp. 102521
Author(s):  
Mostafa Janebi Enayat ◽  
Ali Derakhshan
Keyword(s):  
Second Language ◽  
Vocabulary Size

scholarly journals Measuring the contribution of specialist vocabulary knowledge to academic achievement: disentangling effects of multiple types of word knowledge

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Ahmed Masrai ◽  
James Milton ◽  
Dina Abdel Salam El-Dakhs ◽  
Heba Elmenshawy
Keyword(s):  
Academic Achievement ◽  
Academic Success ◽  
Success Factors ◽  
Vocabulary Knowledge ◽  
The Other ◽  
Word Knowledge ◽  
Academic Vocabulary ◽  
Vocabulary Size ◽  
Two Factors ◽  
The Impact

AbstractThis study investigates the idea that knowledge of specialist subject vocabulary can make a significant and measurable impact on academic performance, separate from and additional to the impact of general and academic vocabulary knowledge. It tests the suggestion of Hyland and Tse (TESOL Quarterly, 41:235–253, 2007) that specialist vocabulary should be given more attention in teaching. Three types of vocabulary knowledge, general, academic and a specialist business vocabulary factors, are tested against GPA and a business module scores among students of business at a college in Egypt. The results show that while general vocabulary size has the greatest explanation of variance in the academic success factors, the other two factors - academic and a specialist business vocabulary - make separate and additional further contributions. The contribution to the explanation of variance made by specialist vocabulary knowledge is double that of academic vocabulary knowledge.

scholarly journals Dynamic Acoustic Unit Augmentation with BPE-Dropout for Low-Resource End-to-End Speech Recognition

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3063
Author(s):  
Aleksandr Laptev ◽  
Andrei Andrusenko ◽  
Ivan Podluzhny ◽  
Anton Mitrofanov ◽  
Ivan Medennikov ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Error Rate ◽  
Rapid Development ◽  
Computational Cost ◽  
Vocabulary Size ◽  
Word Error Rate ◽  
Low Resource ◽  
Steady Improvement ◽  
End To End ◽  
Asr System

With the rapid development of speech assistants, adapting server-intended automatic speech recognition (ASR) solutions to a direct device has become crucial. For on-device speech recognition tasks, researchers and industry prefer end-to-end ASR systems as they can be made resource-efficient while maintaining a higher quality compared to hybrid systems. However, building end-to-end models requires a significant amount of speech data. Personalization, which is mainly handling out-of-vocabulary (OOV) words, is another challenging task associated with speech assistants. In this work, we consider building an effective end-to-end ASR system in low-resource setups with a high OOV rate, embodied in Babel Turkish and Babel Georgian tasks. We propose a method of dynamic acoustic unit augmentation based on the Byte Pair Encoding with dropout (BPE-dropout) technique. The method non-deterministically tokenizes utterances to extend the token’s contexts and to regularize their distribution for the model’s recognition of unseen words. It also reduces the need for optimal subword vocabulary size search. The technique provides a steady improvement in regular and personalized (OOV-oriented) speech recognition tasks (at least 6% relative word error rate (WER) and 25% relative F-score) at no additional computational cost. Owing to the BPE-dropout use, our monolingual Turkish Conformer has achieved a competitive result with 22.2% character error rate (CER) and 38.9% WER, which is close to the best published multilingual system.

A longitudinal study of the role of vocabulary size in priming effects in early childhood

2021 ◽  
Vol 205 ◽  
pp. 105071
Author(s):  
Daniela S. Avila-Varela ◽  
Natalia Arias-Trejo ◽  
Nivedita Mani
Keyword(s):  
Early Childhood ◽  
Longitudinal Study ◽  
Vocabulary Size ◽  
Priming Effects

scholarly journals Global-Local Enhancement Network for NMF-Aware Sign Language Recognition

2021 ◽  
Vol 17 (3) ◽  
pp. 1-19
Author(s):  
Hezhen Hu ◽  
Wengang Zhou ◽  
Junfu Pu ◽  
Houqiang Li
Keyword(s):  
Sign Language ◽  
Daily Life ◽  
The Other ◽  
Challenging Problem ◽  
Language Recognition ◽  
Local Enhancement ◽  
Sign Language Recognition ◽  
Vocabulary Size ◽  
Hand Gestures ◽  
Fine Grained

Sign language recognition (SLR) is a challenging problem, involving complex manual features (i.e., hand gestures) and fine-grained non-manual features (NMFs) (i.e., facial expression, mouth shapes, etc .). Although manual features are dominant, non-manual features also play an important role in the expression of a sign word. Specifically, many sign words convey different meanings due to non-manual features, even though they share the same hand gestures. This ambiguity introduces great challenges in the recognition of sign words. To tackle the above issue, we propose a simple yet effective architecture called Global-Local Enhancement Network (GLE-Net), including two mutually promoted streams toward different crucial aspects of SLR. Of the two streams, one captures the global contextual relationship, while the other stream captures the discriminative fine-grained cues. Moreover, due to the lack of datasets explicitly focusing on this kind of feature, we introduce the first non-manual-feature-aware isolated Chinese sign language dataset (NMFs-CSL) with a total vocabulary size of 1,067 sign words in daily life. Extensive experiments on NMFs-CSL and SLR500 datasets demonstrate the effectiveness of our method.

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