4 research outputs found
Likelihood-based Sensor Calibration using Affine Transformation
An important task in the field of sensor technology is the efficient
implementation of adaptation procedures of measurements from one sensor to
another sensor of identical design. One idea is to use the estimation of an
affine transformation between different systems, which can be improved by the
knowledge of experts. This paper presents an improved solution from Glacier
Research that was published back in 1973. The results demonstrate the
adaptability of this solution for various applications, including software
calibration of sensors, implementation of expert-based adaptation, and paving
the way for future advancements such as distributed learning methods. One idea
here is to use the knowledge of experts for estimating an affine transformation
between different systems. We evaluate our research with simulations and also
with real measured data of a multi-sensor board with 8 identical sensors. Both
data set and evaluation script are provided for download. The results show an
improvement for both the simulation and the experiments with real data
Non-Parallel Training in Voice Conversion Using an Adaptive Restricted Boltzmann Machine
In this paper, we present a voice conversion (VC) method that does not use any parallel data while training the model. VC is a technique where only speaker-specific information in source speech is converted while keeping the phonological information unchanged. Most of the existing VC methods rely on parallel data-pairs of speech data from the source and target speakers uttering the same sentences. However, the use of parallel data in training causes several problems: 1) the data used for the training are limited to the predefined sentences, 2) the trained model is only applied to the speaker pair used in the training, and 3) mismatches in alignment may occur. Although it is, thus, fairly preferable in VC not to use parallel data, a nonparallel approach is considered difficult to learn. In our approach, we achieve nonparallel training based on a speaker adaptation technique and capturing latent phonological information. This approach assumes that speech signals are produced from a restricted Boltzmann machine-based probabilistic model, where phonological information and speaker-related information are defined explicitly. Speaker-independent and speaker-dependent parameters are simultaneously trained under speaker adaptive training. In the conversion stage, a given speech signal is decomposed into phonological and speaker-related information, the speaker-related information is replaced with that of the desired speaker, and then voice-converted speech is obtained by mixing the two. Our experimental results showed that our approach outperformed another nonparallel approach, and produced results similar to those of the popular conventional Gaussian mixture models-based method that used parallel data in subjective and objective criteria
One Model to Rule them all: Multitask and Multilingual Modelling for Lexical Analysis
When learning a new skill, you take advantage of your preexisting skills and
knowledge. For instance, if you are a skilled violinist, you will likely have
an easier time learning to play cello. Similarly, when learning a new language
you take advantage of the languages you already speak. For instance, if your
native language is Norwegian and you decide to learn Dutch, the lexical overlap
between these two languages will likely benefit your rate of language
acquisition. This thesis deals with the intersection of learning multiple tasks
and learning multiple languages in the context of Natural Language Processing
(NLP), which can be defined as the study of computational processing of human
language. Although these two types of learning may seem different on the
surface, we will see that they share many similarities.
The traditional approach in NLP is to consider a single task for a single
language at a time. However, recent advances allow for broadening this
approach, by considering data for multiple tasks and languages simultaneously.
This is an important approach to explore further as the key to improving the
reliability of NLP, especially for low-resource languages, is to take advantage
of all relevant data whenever possible. In doing so, the hope is that in the
long term, low-resource languages can benefit from the advances made in NLP
which are currently to a large extent reserved for high-resource languages.
This, in turn, may then have positive consequences for, e.g., language
preservation, as speakers of minority languages will have a lower degree of
pressure to using high-resource languages. In the short term, answering the
specific research questions posed should be of use to NLP researchers working
towards the same goal.Comment: PhD thesis, University of Groninge