Transfer, similarity or lack of awareness? inconsistencies of German learners in the pronunciation of lot, thought, strut, palm and bath

The current study presents acoustic analyses of non-high back vowels and low central vowels in the lexical sets LOT, THOUGHT, STRUT, PALM and BATH as pronounced by German learners of English. The main objective is to show that learners of English at university level are highly inconsistent in approximating the vowels of their self-chosen target accents British English (BrE) and American English (AmE). To that end, the acoustic qualities of the English vowels of learners are compared to their native German vowels and to the vowels of native speakers of BrE and AmE. In order to facilitate statements about the effect of increased experience, the study differentiates between students in their first year at university and in their third year or later. The results obtained are highly variable: In some cases the learners transfer their L1 vowels to English, other cases show clear approximations to the target vowels, while other cases again document the production of new vowels neither found in German nor in English. However, close approximation to the target vowels only sometimes correlates with higher proficiency. This might be an indicator of a low level of awareness of systematic differences between the BrE and AmE vowel systems. But the data also indicate that the more advanced learners produce more distinct AmE BATH vowels and BrE THOUGHT vowels than the less advanced learners, which points to a partial increase of awareness resulting from increased experience. All in all it seems that raising the awareness of differences between target accents in L2 instruction is necessary if the envisage goal is for learners to reach near-native pronunciation

Transfer Learning for Speech and Language Processing

Transfer learning is a vital technique that generalizes models trained for one setting or task to other settings or tasks. For example in speech recognition, an acoustic model trained for one language can be used to recognize speech in another language, with little or no re-training data. Transfer learning is closely related to multi-task learning (cross-lingual vs. multilingual), and is traditionally studied in the name of `model adaptation'. Recent advance in deep learning shows that transfer learning becomes much easier and more effective with high-level abstract features learned by deep models, and the `transfer' can be conducted not only between data distributions and data types, but also between model structures (e.g., shallow nets and deep nets) or even model types (e.g., Bayesian models and neural models). This review paper summarizes some recent prominent research towards this direction, particularly for speech and language processing. We also report some results from our group and highlight the potential of this very interesting research field.Comment: 13 pages, APSIPA 201

A Resource-Based View Of International Human Resources: Toward A Framework of Integrative and Creative Capabilities

Drawing on organizational learning and MNC perspectives, we extend the resource-based view to address how international human resource management provides sustainable competitive advantage. We develop a framework that emphasizes and extends traditional assumptions of the resource-based view by identifying the learning capabilities necessary for a complex and changing global environment. These capabilities address how MNCs might both create new HR practices in response to local environments and integrate existing HR practices from other parts of the firm (affiliates, regional headquarters, and global headquarters). In an effort to understand the nature of such capabilities, we discuss aspects of human capital, social capital, and organizational capital that might be linked to their development. Page

The role of Intangible Assets in the Relationship between HRM and Innovation: A Theoretical and Empirical Exploration

This paper, as far as known, provides a first attempt to explore the role of intellectual capital (IC) and knowledge management (KM) in an integrative way between the relationship of human resource (HR) practices and two types of innovation (radical and incremental). More specifically, the study investigates two sub-components of IC – human capital and organizational social capital. At the same time, four KM channels are discussed, such as knowledge creation, acquisition, transfer and responsiveness.\ud The research is a part of a bigger project financed by the Ministry of Economic Affairs and the province of Overijssel in the Netherlands. The project studies the ‘competencies for innovation’ and is conducted in collaboration with innovative companies in the Eastern part of the Netherlands. \ud An exploratory survey design with qualitative and quantitative data is used for\ud investigating the topic in six companies from industrial and service sector in the region of Twente, the Netherlands. Mostly, the respondents were HR directors. The findings showed that some parts of IC and KM configurations were related to different types of innovation. To make the picture even more complicated, HR practices were sometimes perceived interchangeably with IC and KM by HR directors. Overall, the whole picture about the relationships stays unclear and opens a floor for further research

Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective

This paper takes a problem-oriented perspective and presents a comprehensive review of transfer learning methods, both shallow and deep, for cross-dataset visual recognition. Specifically, it categorises the cross-dataset recognition into seventeen problems based on a set of carefully chosen data and label attributes. Such a problem-oriented taxonomy has allowed us to examine how different transfer learning approaches tackle each problem and how well each problem has been researched to date. The comprehensive problem-oriented review of the advances in transfer learning with respect to the problem has not only revealed the challenges in transfer learning for visual recognition, but also the problems (e.g. eight of the seventeen problems) that have been scarcely studied. This survey not only presents an up-to-date technical review for researchers, but also a systematic approach and a reference for a machine learning practitioner to categorise a real problem and to look up for a possible solution accordingly