Exploring the Encoding Layer and Loss Function in End-to-End Speaker and Language Recognition System

In this paper, we explore the encoding/pooling layer and loss function in the end-to-end speaker and language recognition system. First, a unified and interpretable end-to-end system for both speaker and language recognition is developed. It accepts variable-length input and produces an utterance level result. In the end-to-end system, the encoding layer plays a role in aggregating the variable-length input sequence into an utterance level representation. Besides the basic temporal average pooling, we introduce a self-attentive pooling layer and a learnable dictionary encoding layer to get the utterance level representation. In terms of loss function for open-set speaker verification, to get more discriminative speaker embedding, center loss and angular softmax loss is introduced in the end-to-end system. Experimental results on Voxceleb and NIST LRE 07 datasets show that the performance of end-to-end learning system could be significantly improved by the proposed encoding layer and loss function.Comment: Accepted for Speaker Odyssey 201

Foreign language learning needs in higher education: Reasons for convergence and accountability

Needs analysis is a relevant issue in language teaching as its final aim is to enhance the language learning process by setting realistic course objectives. This paper briefly reviews past and present approaches to the concept of “needs” and “needs analysis” in foreign language (FL) methodology with particular reference to the higher education context and the post-Bologna reform. It discusses the complex interplay of addressing target and learning FL needs by gathering data from different stakeholder groups and making use of different data collection instruments. Then, it explores current trends in needs analysis research and explains how “competences” and “profiling” have surpassed “needs” and “needs analysis” in the educational systems under the European Space of Higher Education. Last, this study addresses the implications of adopting a more critical, quality-driven approach to FL needs analysis with a view to attaining a more participatory and accountable higher education