Characterization of cassava production systems in Vietnam.

Using a nationally representative survey of cassava-growing households in Vietnam and a robust method of varietal identification based on DNA fingerprinting, this paper provides a broad picture of cassava production and socio-economic characteristics of cassava producers in the country. It presents a descriptive analysis of cassava production practices, varietal use, varietal preferences, as well as cassava utilization, and marketing. Results indicate that more than 85% of the cassava area in Vietnam is planted to improved varieties. The average yield at national level is 19 tons per hectare. About 69% of total cassava produced per household is sold as either fresh roots and/or dried chips. The remaining 31% is either for own consumption or for livestock feed. Of all the six regions surveyed, the Southeast is characterized by the most intensive cassava production practices. It also has the largest average cassava area per household, the highest percentage of tractor use, and a higher percentage of fertilizer application on cassava fields. The findings suggest that there are huge challenges for sustainable cassava intensification, specifically in identifying the needs for market diversification, dealing with emerging pests and diseases, and implementing adequate soil management practices. This is particularly challenging in a system that is driven by the need to maximize output with minimum investment. Future research and development should focus on integrated value chain development with multiple actors focusing attention on integrated pest and disease management, seed systems development, breeding for resistance and earliness, and climate change adaptation, among others

Voice conversion using deep neural networks

This thesis focuses on techniques to improve the performance of voice conversion. Voice conversion modifies the recorded speech of a source speaker towards a given target speaker. The resultant speech is to sound like the target speaker with the language content unchanged. This technology has been applied to create personalized voice in text-to-speech or virtual avatar, speech-to-singing synthesis or spoofing attacks in speaker verification systems. To perform voice conversion, the usual approach is to create a conversion functions which is applied on the source speaker’s speech features such as timbre and prosodic features, to generate the corresponding target features. In this past decade, most of voice conversion researches had focused on spectral mapping, i.e. conversion of the features representing the timbre characteristics in a frame by frame manner. In chapter 3, we investigate a comprehensive approach to train the conversion function using DNN which considers both timbre and prosodic features simultaneously. For better modelling, we have used high-dimension spectral features. However, this further worsen the ability to robustly train a DNN which typically requires large amount of training data. To overcome the issue of limited training data, we propose a new pretraining process using autoencoder. The experimental results show the proposed comprehensive framework with pretraining performs better than conventional voice conversion systems including the state-of-the-art GMM-based system. The technique introduced in chapter 3 only learns a DNN system to convert between a pair of speaker. To reduce the need for parallel training data of new speaker pair, in chapter 4 we examine a novel DNN adaptation technology for voice conversion by including two bias vector representing both source and target speaker. By this configuration, new speaker pair conversion are archived. Our preliminary results show that conversion to new target speakers’ voices could be achieved.Master of Engineering (SCE

O pewnych własnościach probabilistycznych rozwiązania stochastycznego równania całkowego

Zadanie pt. „Digitalizacja i udostępnienie w Cyfrowym Repozytorium Uniwersytetu Łódzkiego kolekcji czasopism naukowych wydawanych przez Uniwersytet Łódzki” nr 885/P-DUN/2014 zostało dofinansowane ze środków MNiSW w ramach działalności upowszechniającej naukę

System fusion for high-performance voice conversion

Recently, a number of voice conversion methods have been developed. These methods attempt to improve conversion performance by using diverse mapping techniques in various acoustic domains, e.g. high-resolution spectra and low-resolution Mel-cepstral coefficients. Each individual method has its own pros and cons. In this paper, we introduce a system fusion framework, which leverages and synergizes the merits of these state-of-the-art and even potential future conversion methods. For instance, methods delivering high speech quality are fused with methods capturing speaker characteristics, bringing another level of performance gain. To examine the feasibility of the proposed framework, we select two state-of-the-art methods, Gaussian mixture model and frequency warping based systems, as a case study. Experimental results reveal that the fusion system outperforms each individual method in both objective and subjective evaluation, and demonstrate the effectiveness of the proposed fusion framework.Published versio

An automatic voice conversion evaluation strategy based on perceptual background noise distortion and speaker similarity

Voice conversion aims to modify the characteristics of one speaker to make it sound like spoken by another speaker without changing the language content. This task has attracted considerable attention and various approaches have been proposed since two decades ago. The evaluation of voice conversion approaches, usually through time-intensive subject listening tests, requires a huge amount of human labor. This paper proposes an automatic voice conversion evaluation strategy based on perceptual background noise distortion and speaker similarity. Experimental results show that our automatic evaluation results match the subjective listening results quite well. We further use our strategy to select best converted samples from multiple voice conversion systems and our submission achieves promising results in the voice conversion challenge (VCC2016).Published versio