O uso de redes neurais auto-organizáveis na análise da transferência de conhecimentos prosódico em aprendizes brasileiros de língua inglesa

Abstract

The objective of this dissertation was to investigate how the prosodic knowledge is organized in an early stage of L2 acquisition in Brazilian learners of English with the help of a connectionist neural network. The approach proposed in this research is first, to quantify the utterances of L2 learners in the form of LPC coefficients and other linguistic/phonetics features that can represent the phenomenon studied here (Transfer of the prosodic knowledge from Portuguese to English). This process is called speech feature extraction, an important step in the connectionist approach to speech processing. Second, since certain features of the lexical item or sentence produced by each learner are determined, these data are entered into the neural network to analyze the statistical properties (regularities) of the set of speakers as a whole. Third, visualization tools are used to analyze how the network organizes speakers and what information is most relevant to this process of group formation (e.g. proficiency level, a certain characteristic or property of speech, among others). The network is known as Self-Organizing Map (Self-Organizing Map, SOM). The SOM organizes speakers for similarity degree in well-defined groups (clusters). Application of SOM in this context is therefore innovative. The SOM network is implemented in Matlab environment using the SOMtoolbox package, which is a set of programming routines developed by the research group in Finland, also the inventors of the SOM. The simulation results indicate that SOM might be used more frequently to assess the degree of distance that a group of learners is to the group of native speakers. Thus, a neural network might be used as a tool in the context of determining the level of foreign language proficiency.