Year: 2017
Type: dissertation
Abstract: A new machine learning paradigm, called deep learning, has accelerated the development of state-of-the-art systems in various research domains.Deep learning leverages a sophisticated network of non-linear units and their connections to learn multiple levels of abstraction from the data.Each of these units is inspired by a model of organic brain cell which is known as a neuron.A deep neural network (DNN), which contains millions of neurons with their own parameters, allows the model freely optimizes its feature representations for particular tasks.This capability has been proven to be an universal function approximators which properly benefits the processing of complex signals, for instance, the voice signal.These architectures and algorithms have been the core of the recent ground breaking approaches to automatic speech processing which includes automatic speech recognition (ASR), speaker recognition (SR) and language identification (LID) tasks.In particular, a combination of both deep learning and acoustic modeling has brought about breakthroughs in ASR, especially, an end-to-end SR system can interpret speech directly from its most primitive spectral form.The end-to-end design enhances completeness and is more flexible adapting to wide range of voice signals without the requirement of excessive hand-engineering features, moreover, it also increase the reliability by reducing the stacked deficiency of multiple components.However, similar exploration in LID is quite limited by the attention of research community, regardless the connection to speech technology of all three research fields.This Master Thesis is motivated to unify the most recent advances in deep learning for speech processing, in order to solve end-to-end LID tasks.The work aims for multiperspective approaches in which the investigation are different combination of the recent state-of-the-art deep networks' architectures and conventional LID algorithms.The approaches are evaluated on Language Recognition Evaluation 2015 (LRE'15) from National Institute of Standards and Technology (NIST), the corpus are recorded audio in heterogeneous environments and languages which are considered the newest challenging LID dataset.
Author: Trung Ngô Trọng
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3204444259