A Two-Phase Damped-Exponential Model for Speech Synthesis

It is well known that there is room for improvement in the resultant quality of speech synthesizers in use today. This research focuses on the improvement of speech synthesis by analyzing various models for speech signals. An improvement in synthesis quality will benefit any system incorporating speech synthesis. Many synthesizers in use today use linear predictive coding (LPC) techniques and only use one set of vocal tract parameters per analysis frame or pitch period for pitch-synchronous synthesizers. This work is motivated by the two-phase analysis-synthesis model proposed by Krishnamurthy. In lieu of electroglottograph data for vocal tract model transition point determination, this work estimates this point directly from the speech signal. The work then evaluates the potential of the two-phase damped-exponential model for synthetic speech quality improvement. LPC and damped-exponential models are used for synthesis. Statistical analysis of data collected in a subjective listening test indicates a statistically significant improvement (at the 0.05 significance level) in quality using this two-phase damped-exponential model over single-phase LPC, single-phase damped-exponential and two-phase LPC for the speakers, sentences, and model orders used. This subjective test shows the potential for quality improvement of synthesized speech and supports the need for further research and testing

Assessment of carbon in woody plants and soil across a vineyard-woodland landscape

<p>Abstract</p> <p>Background</p> <p>Quantification of ecosystem services, such as carbon (C) storage, can demonstrate the benefits of managing for both production and habitat conservation in agricultural landscapes. In this study, we evaluated C stocks and woody plant diversity across vineyard blocks and adjoining woodland ecosystems (wildlands) for an organic vineyard in northern California. Carbon was measured in soil from 44 one m deep pits, and in aboveground woody biomass from 93 vegetation plots. These data were combined with physical landscape variables to model C stocks using a geographic information system and multivariate linear regression.</p> <p>Results</p> <p>Field data showed wildlands to be heterogeneous in both C stocks and woody tree diversity, reflecting the mosaic of several different vegetation types, and storing on average 36.8 Mg C/ha in aboveground woody biomass and 89.3 Mg C/ha in soil. Not surprisingly, vineyard blocks showed less variation in above- and belowground C, with an average of 3.0 and 84.1 Mg C/ha, respectively.</p> <p>Conclusions</p> <p>This research demonstrates that vineyards managed with practices that conserve some fraction of adjoining wildlands yield benefits for increasing overall C stocks and species and habitat diversity in integrated agricultural landscapes. For such complex landscapes, high resolution spatial modeling is challenging and requires accurate characterization of the landscape by vegetation type, physical structure, sufficient sampling, and allometric equations that relate tree species to each landscape. Geographic information systems and remote sensing techniques are useful for integrating the above variables into an analysis platform to estimate C stocks in these working landscapes, thereby helping land managers qualify for greenhouse gas mitigation credits. Carbon policy in California, however, shows a lack of focus on C stocks compared to emissions, and on agriculture compared to other sectors. Correcting these policy shortcomings could create incentives for ecosystem service provision, including C storage, as well as encourage better farm stewardship and habitat conservation.</p