Children's recall of words spoken in their first and second language : Effects of Signal-to-Noise Ratio and Reverberation Time

Speech perception runs smoothly and automatically when there is silence in the background, but when the speech signal is degraded by background noise or by reverberation, effortful cognitive processing is needed to compensate for the signal distortion. Previous research has typically investigated the effects of signal-to-noise ratio (SNR) and reverberation time in isolation, whilst few have looked at their interaction. In this study, we probed how reverberation time and SNR influence recall of words presented in participants' first- (L1) and second-language (L2). A total of 72 children (10 years old) participated in this study. The to-be-recalled wordlists were played back with two different reverberation times (0.3 and 1.2 s) crossed with two different SNRs (+3 dBA and +12 dBA). Children recalled fewer words when the spoken words were presented in L2 in comparison with recall of spoken words presented in L1. Words that were presented with a high SNR (+12 dBA) improved recall compared to a low SNR (+3 dBA). Reverberation time interacted with SNR to the effect that at +12 dB the shorter reverberation time improved recall, but at +3 dB it impaired recall. The effects of the physical sound variables (SNR and reverberation time) did not interact with language. © 2016 Hurtig, Keus van de Poll, Pekkola, Hygge, Ljung and Sörqvist.

The functions of Japanese Sound Symbolic Words in Different Types of Texts and Their Translation

Though sound symbolic words (onomatopoeia and mimetic words, or giongo and gitaigo in Japanese) exist in other languages, it would not be so easy to compare them to those in Japanese. This is because unlike in Japanese, in many other languages (here we see English and Spanish) sound symbolic words do not have distinctive forms that separate them immediately from the rest of categories of words. In Japanese, a sound symbolic word has a radical (that is based on the elaborated Japanese sound symbolic system), and often a suffix that shows subtle nuance. Together they give the word a distinctive form that differentiates it from other categories of words, though its grammatical functions could vary, especially in the case of mimetic words (gitaigo). Without such an obvious feature, in other languages, it would not be always easy to separate sound symbolic words from the rest. These expressions are extremely common and used in almost all types of text in Japanese, but their elaborated sound symbolic system and possibly their various grammatical functions are making giongo and gitaigo one of the most difficult challenges for the foreign students and translators. Studying the translation of these expressions into other languages might give some indication related to the comparison of Japanese sound symbolic words and those in other languages. Though sound symbolic words are present in many types of texts in Japanese, their functions in traditional forms of text (letters only) and manga (Japanese comics)are different and they should be treated separately. For example, in traditional types of text such as novels, the vast majority of the sound symbolic words used are mimetic words (gitaigo) and most of them are used as adverbs, whereas in manga, the majority of the sound symbolic words used (excluding those appear within the speech bubbles) are onomatopoeias (giongo) and often used on their own (i.e. not as a part of a sentence). Naturally, the techniques used to translate these expressions in the above two types of documents differ greatly. The presentation will focus on i) grammatical functions of Japanese sound symbolic words in traditional types of texts (novels/poems) and in manga works, and ii) whether their features and functions are maintained (i.e. whether they are translated as sound symbolic words) when translated into other languages (English and Spanish). The latter point should be related to a comparison of sound symbolic words in Japanese and other languages, which will be also discussed.

Vocabulary learning and homework in the upper elementary EFL classroom.

Vocabulary homework is a common feature in the English subject in Sweden. Often the pupils are given a list of words they are to memorize for a pending test. In this literature review the author aims to analyze what the current research says about homework and how homework can be used effectively for EFL learners in elementary school, with a focus on both homework and vocabulary learning research. Cognitive linguistics has been used as a theoretical perspective to help answer the research questions. Results indicate that homework has limited effect on younger learners and should not be used, while, some researchers claim that it can be effective if introduced properly. Regarding vocabulary learning, it is important that vocabulary is relevant to the learner and that words are taught through a meaningful context. Therefore, vocabulary homework for EFL learners in elementary school should consist of words and phraseology which have a personal relevance to the learner, or key words for subjects taught in class. The conclusion of the study is that it is up to the teachers to determine if they should use vocabulary homework or not when teaching EFL, as long as the decision is based on current research.

A machine learning approach for recognising woody plants on railway trackbeds

The purpose of this work in progress study was to test the concept of recognising plants using images acquired by image sensors in a controlled noise-free environment. The presence of vegetation on railway trackbeds and embankments presents potential problems. Woody plants (e.g. Scots pine, Norway spruce and birch) often establish themselves on railway trackbeds. This may cause problems because legal herbicides are not effective in controlling them; this is particularly the case for conifers. Thus, if maintenance administrators knew the spatial position of plants along the railway system, it may be feasible to mechanically harvest them. Primary data were collected outdoors comprising around 700 leaves and conifer seedlings from 11 species. These were then photographed in a laboratory environment. In order to classify the species in the acquired image set, a machine learning approach known as Bag-of-Features (BoF) was chosen. Irrespective of the chosen type of feature extraction and classifier, the ability to classify a previously unseen plant correctly was greater than 85%. The maintenance planning of vegetation control could be improved if plants were recognised and localised. It may be feasible to mechanically harvest them (in particular, woody plants). In addition, listed endangered species growing on the trackbeds can be avoided. Both cases are likely to reduce the amount of herbicides, which often is in the interest of public opinion. Bearing in mind that natural objects like plants are often more heterogeneous within their own class rather than outside it, the results do indeed present a stable classification performance, which is a sound prerequisite in order to later take the next step to include a natural background. Where relevant, species can also be listed under the Endangered Species Act.