Generating multiple-accent pronunciations for TTS using joint sequence model interpolation

Standard grapheme-to-phoneme (G2P) systems are trained using a homogeneous lexicon, for example one associated with a particular accent. In practice, a synthesis system may be required to handle multiple accents. Furthermore, a speaker rarely has a pure accent; accents vary continuously within and between regions of a country. Generating phonetic sequences for each accent is possible, but combining them to yield a single synthesis pronunciation is highly challenging. To address this problem, this paper considers a space of accents. The bases for these spaces are defined by statistical G2P models in the form of graphone models. A linear combination of these models define the accent space. By selecting a point in this continuous space, it is possible to specify the accent for an individual speaker. The performance of this approach is evaluated using an accent space defined by American, Scottish and British English. By moving around the accent space, it is shown that it is possible to synthesize speech from all these accents as well as a range of intermediate points

Photo-realistic expressive text to talking head synthesis

A controllable computer animated avatar that could be used as a natural user interface for computers is demonstrated. Driven by text and emotion input, it generates expressive speech with corresponding facial movements. To create the avatar, HMM-based text-to-speech synthesis is combined with active appearance model (AAM)-based facial animation. The novelty is the degree of control achieved over the expressiveness of both the speech and the face while keeping the controls simple. Controllability is achieved by training both the speech and facial parameters within a cluster adaptive training (CAT) framework. CAT creates a continuous, low dimensional eigenspace of expressions, which allows the creation of expressions of different intensity (including ones more intense than those in the original recordings) and combining different expressions to create new ones. Results on an emotion-recognition task show that recognition rates given the synthetic output are comparable to those given the original videos of the speaker. Copyright © 2013 ISCA

Excessive secretion of IL-8 by skeletal muscle in type 2 diabetes impairs tube growth: potential role of PI3K and the Tie2 receptor

Reduced capillary density is a feature of skeletal muscle (SkM) in type 2 diabetes (T2D), which is associated with multiple metabolic and functional abnormalities. SkM has been identified as a secretory tissue, releasing myokines that regulate multiple processes, including vascularization. We sought to determine how myokines secreted from T2D myotubes might influence SkM angiogenesis. Conditioned media (CM) were generated by myotubes from T2D and nondiabetic (ND) subjects. Primary human endothelial cells (HUVEC) and SkM explants were exposed to CM or recombinant myokines, and tube number or capillary outgrowth was determined as well as measurement of protein expression and phosphorylation. CM from ND myotubes stimulated tube formation of HUVEC to a greater extent than T2D myotubes (T2D-CM = 100%, ND-CM = 288 ± 90% after 48 h, P < 0.05). The effects of T2D myotube CM were mediated by IL-8, not IL-15 or GROα, and were due not to cell damage but rather through regulating tube production and maintenance (response to T2D-IL-8 = 100%, response to ND-IL-8 = 263 ± 46% after 48 h, P < 0.05). A similar effect was seen in SkM explants with exposure to IL-8. The dose-dependent effect of IL-8 on tube formation was also observable in the PI3K and FAK signaling pathways and mediated at least in part by PI3K, leading to regulation of Tie2 expression. These results suggest that elevated levels of IL-8 secreted from T2D myotubes create a muscle microenvironment that supports reduced capillarization in T2D. Impaired vascularization of SkM limits the availability of substrates, including glucose and contributes to the T2D phenotype