Adaptation of mixed-excitation linear predictive (MELP) model for application in voice conversion is presented. The adapted model features only numerical parameters which can be used for phonetic space transformation from source to target speaker using methods of machine learning. The validity of the model was demonstrated by applying transformation to both the pitch and the spectral envelope o...

Previously, we proposed two voice-to-phoneme conversion algorithms for speaker-independent voice-tag creation specifically targeted at applications on embedded platforms, an environment sensitive to CPU and memory resource consumption [1]. These two algorithms (batch mode and sequential) were applied in a same-language context, i.e., both acoustic model training and voice-tag creation and appli...

Spoken language conversion (SLC) aims to generate utterances in the voice of a speaker but in a language unknown to them, using speech synthesis systems and speech processing techniques. Previous approaches to SLC have been based on cross-language voice conversion (VC), which has underlying assumptions that ignore phonetic and phonological differences between languages, leading to a reduction i...

We present in this paper a voice conversion (VC) method for a person with an articulation disorder resulting from athetoid cerebral palsy. The movement of such speakers is limited by their athetoid symptoms, and their consonants are often unstable or unclear, which makes it difficult for them to communicate. In this paper, exemplar-based spectral conversion using nonnegative matrix factorizatio...

We present in this paper a voice conversion (VC) method for a person with an articulation disorder resulting from athetoid cerebral palsy. The movements of such speakers are limited by their athetoid symptoms, and their consonants are often unstable or unclear, which makes it difficult for them to communicate. In this paper, exemplar-based spectral conversion using Nonnegative Matrix Factorizat...

We present an evaluation of the perception of foreign-accented natural and synthetic speech in comparison to accent-reduced synthetic speech. Our method for foreign accent conversion is based on mapping of Hidden Semi-Markov Model states between accented and non-accented voice models and does not need an average voice model of accented speech. We employ the method on recorded data of speakers w...

The aim of this paper is to improve the naturalness of speech using a medical device such as an electrolarynx. There are several problems associated with using existing electrolarynxes, such as the fact the loud volume of the electrolarynx itself might disturb smooth interpersonal communication, and that the generated speech is unnatural. We propose a novel speaking-aid system for total larynge...

This paper proposes the application of evolutionary computation, a stochastic search technique that parallels the evolution of living organisms, to parameter adjustment for voice conversion, and reports on several experimental results applicable to the fitting of prosodic coefficients. Here, because of the difficulty involved in providing a clear fitness function for evaluating evolutionary com...

This paper presents a voice conversion technique using speaker-dependent Restricted Boltzmann Machines (RBM) to build highorder eigen spaces of source/target speakers, where it is easier to convert the source speech to the target speech than in the traditional cepstrum space. We build a deep conversion architecture that concatenates the two speakerdependent RBMs with neural networks, expecting ...