Vassilios Diakoloukas

• PhD
• Laboratory Teaching Staff at Technical University of Crete

The behavior and possible contamination risk due to the presence of potentially harmful metals (PHM) were studied based on 2250 soil samples that were collected in a 5-year period (2013–2017) from the plain of Thessaly (prefectures of Karditsa, Trikala, and Larissa). The vertical distribution of metals was also investigated from sample profiles at...

The use of Reinforcement Learning (RL) approaches for dialogue policy optimization has been the new trend for dialogue management systems. Several methods have been proposed, which are trained on dialogue data to provide optimal system response. However, most of these approaches exhibit performance degradation in the presence of noise, poor scalabi...

Statistical Dialogue Systems (SDS) have proved their humongous potential over the past few years. However, the lack of efficient and robust representations of the belief state (BS) space refrains them from revealing their full potential. There is a great need for automatic BS representations, which will replace the old hand-crafted, variable-length...

Linear Dynamical Models (LDMs) have been used in speech synthesis recently as an alternative to hidden Markov models (HMMs). Among the advantages of LDMs are the ability to capture the dynamics of speech and the achievement of synthesized speech quality similar to HMM-based speech systems on a smaller footprint. However, such as in the HMM case, LD...

We present recent developments towards building a speech synthesis system completely based on Linear Dynamical Models (LDMs). Specifically, we describe a decision tree-based context clustering approach to LDM-based speech synthesis and an algorithm for parameter generation using global variance with LDMs. In order to capture the speech dynamics, LD...

Hidden Markov models (HMMs) are becoming the dominant approach for text-to-speech synthesis (TTS). HMMs provide an attractive acoustic modeling scheme which has been exhaustively investigated and developed for many years. Modern HMM-based speech synthesizers have approached the quality of the best state-of-the-art unit selection systems. However, w...

This paper describes a Viterbi-like decoding algorithm applied on segment-models based on linear dynamic systems (LDMs). LDMs are a promising acoustic modeling scheme which can alleviate several of the limitations of the popular Hidden Markov Models (HMMs). There are several implementations of LDMs that can be found in the literature. For our decod...

Although hidden Markov models (HMMs) provide a relatively efficient modeling framework for speech recognition, they suffer from several shortcomings which set upper bounds in the performance that can be achieved. Alternatively, linear dynamic models (LDM) can be used to model speech segments. Several implementations of LDM have been proposed in the...

The porting of a speech recognition system to a new language is usually a time-consuming and expensive process since it requires collecting, transcribing, and processing a large amount of language-specific training sentences. This work presents techniques for improved cross-language transfer of speech recognition systems to new target languages. Su...

In this work, we present the creation of the first Greek Speech Corpus and the implementation of a Dictation System for workflow improvement in the field of journalism. The current work was implemented under the project called Logotypografia (Logos = logos, speech and Typografia = typography) sponsored by the General Secretariat of Research and Dev...

Speaker adaptation is recognized as an essential part of today’s large-vocabulary automatic speech recognition systems. A family of techniques that has been extensively applied for limited adaptation data is transformation-based adaptation. In transformation-based adaptation we partition our parameter space in a set of classes, estimate a transform...

The recognition accuracy in previous large vocabulary automatic speech recognition (ASR) systems is highly related to the existing mismatch between the training and testing sets. For example, dialect differences across the training and testing speakers result in a significant degradation in recognition performance. Some popular adaptation approache...

The recognition accuracy in recent large vocabulary Automatic Speech Recognition (ASR) systems is highly related to the existing mismatch between the training and test sets. For example, dialect differences across the training and testing speakers result to a significant degradation in recognition performance. Some popular adaptation approaches imp...

Several adaptation approaches have been proposed in an effort to improve the speech recognition performance in mismatched conditions. However, the application of these approaches had been mostly constrained to the speaker or channel adaptation tasks. In this paper, we first investigate the effect of mismatched dialects between training and testing...