Iterative Compression of End-to-End ASR Model using AutoML

Increasing demand for on-device Automatic Speech Recognition (ASR) systems has resulted in renewed interests in developing automatic model compression techniques. Past research have shown that AutoML-based Low Rank Factorization (LRF) technique, when applied to an end-to-end Encoder-Attention-Decoder style ASR model, can achieve a speedup of up to 3.7x, outperforming laborious manual rank-selection approaches. However, we show that current AutoML-based search techniques only work up to a certain compression level, beyond which they fail to produce compressed models with acceptable word error rates (WER). In this work, we propose an iterative AutoML-based LRF approach that achieves over 5x compression without degrading the WER, thereby advancing the state-of-the-art in ASR compression

Development of a System for Analysing Method Names in Java Source Code

Naming code can seem like a simple task, however finding a good name can be rather challenging. Entity names should be consistent and brief yet comprehensive when representing the information each entity hold. What is considered a good name can be highly debatable, although it usually involves descriptive names that can contribute to readability and comprehensibility of source code. Bad code names can cause uncertainty, potential future bugs and be misleading. For this reason, the task of naming code is vital, hence there is a need of a system to improve and maintain it. To develop such a system, there are requirements required to be specified to define the expected implementation for certain entity names. These requirements are encoded into software in a domain-specific language, granting executable code to be generated from the expressed requirements. As a result, this name analysis tool provides programmers to perform code analysis on Java source code checking if the entities act in accordance with the requirements of their names. Additionally, the result shows insights of how contributions from linguistics can be valuable for software development and can be used to analyse software languages, such as entity names.Masteroppgave i informatikkINF399MAMN-PROGMAMN-IN