We study the representation and encoding of phonemes in a recurrent neural network model of grounded speech. We use a model which processes images and their spoken descriptions, and projects the visual and auditory representations into the same semantic space. We perform a number of analyses on how information about individual phonemes is encoded in the MFCC features extracted from the speech signal, and the activations of the layers of the model. Via experiments with phoneme decoding and phoneme discrimination we show that phoneme representations are most salient in the lower layers of the model, where low-level signals are processed at a fine-grained level, although a large amount of phonological information is retain at the top recurrent layer. We further find out that the attention mechanism following the top recurrent layer significantly attenuates encoding of phonology and makes the utterance embeddings much more invariant to synonymy. Moreover, a hierarchical clustering of phoneme representations learned by the network shows an organizational structure of phonemes similar to those proposed in linguistics.

Providing first language (L1) translations in L2 vocabulary interventions may be beneficial for L2 vocabulary learning. However, in linguistically diverse L2 classrooms, teachers cannot provide L1 translations to all children. Social robots do offer such opportunities, as they can be programmed to speak any combination of languages. This study investigates whether providing L1 translations in a robot‐assisted L2 vocabulary training facilitates children's learning. Participants were Turkish‐Dutch kindergartners (n = 67) who were taught six Dutch (L2) words for which they knew the L1 (Turkish), but not the L2 Dutch form. Half of these words were taught by a Turkish‐Dutch bilingual robot, alongside their Turkish translations; the other half by a monolingual Dutch robot. Children also completed Dutch and Turkish receptive vocabulary tests. Results of generalized linear regression models indicated better performance in the Dutch‐only condition than in the Turkish‐Dutch condition. Children with well‐developed Turkish and Dutch vocabulary knowledge outperformed children with less well‐developed vocabulary knowledge. The majority of children preferred working with the bilingual robot, but children's preference did not affect word learning. Thus, contrary to our prediction, we found no evidence for a facilitating effect of providing L1 translations through a robot on bilingual children's L2 word learning.

Aims and objectives/purpose/research questions: This study compares transfer from Dutch to German by native Dutch speakers who learned German as a second language (forward transfer: L1 to L2) and by native German speakers who are living in the Netherlands (reverse transfer: L2 to L1). The aim of this comparison is to see whether both groups experience the same kind of transfer (i.e., transferring the Dutch preference for prepositional phrases in the postfield position to German) and whether the extent of transfer depends on their language use. Design/methodology/approach: We compiled a corpus (2,908,154 words) consisting of German e-mails written by native Dutch speakers ( n = 21) and native German speakers living in the Netherlands ( n = 9). In addition, speakers filled in the BLP (bilingual language profile) test. Data and analysis: The corpus was analyzed for the speakers’ placement of prepositional phrases, and we linked their answers on the BLP test to their language use in the corpus data. Findings/conclusions: Our data show that the native Dutch speakers use the postfield position more frequently in their German than the native German speakers do. Besides, for both groups, the postfield use was related to the speakers’ use of Dutch as well as German varieties that are influenced by Dutch. Originality: This study directly compares forward and reverse transfer using a large corpus of written German texts, and it links both types of transfer to the speakers’ language use. In doing so, the study shows that the mechanism of entrenchment is likely to underlie transfer in both cases. Significance/implications: The results of this study are in line with a usage-based approach: there is extensive individual variation between speakers regarding their extent of transfer, which partly can be attributed to the speakers’ language use, both in the case of forward and reverse transfer.

Bilingual speakers of typologically closely related languages tend to frequently experience language transfer, which suggests that similarity between languages is likely to play an important role in the transfer process. In this paper, we explore how three different types of similarity affect transfer of light verb constructions (lvc s), such as take a walk or set an alarm, from Dutch to German by native German speakers living in the Netherlands, namely: (a) similarity to existing constructions, (b) surface similarity based on whether the noun in the lvc is a cognate in Dutch and German, and (c) similarity in the light verb’s collocational contexts. The results suggest that all three types of similarity influence transfer: speakers add similar constructions to their language and they drop existing ones that happen to be less similar, ultimately facilitating convergence across the speakers’ languages.

This study puts the usage-based assumption that our linguistic knowledge is based on usage to the test. To do so, we explore individual variation in speakers’ language use as established based on corpus data – both in terms of frequency of use (as a proxy for entrenchment) and productivity of use (as a proxy for schematization) – and link this variation to the same participants’ responses in an experimental judgment task. The empirical focus is on transfer by native German speakers living in the Netherlands, who oftentimes experience transfer from their second language Dutch to their native language German regarding the placement of prepositional phrases. The analyses show a large amount of variation in both the corpus and experimental data with a strong link across data types: individual speakers’ usage – but not the usage by other speakers – is a significant predictor for the speakers’ judgments. These results strongly suggest that, in line with a usage-based approach, variation between speakers in experimental tasks is linked to their variation in usage. At the same time, such usage-based predictions do not explain all of the variation, suggesting that other individual factors are also at play in such experimental tasks.