Voice conversion can be formulated as finding a mapping function which transforms the features of the source speaker to those of the target speaker. Gaussian mixture model (GMM) based conversion is commonly used, but it is subject to overfitting. In this paper, we propose to use partial least squares (PLS) based transforms in voice conversion. To prevent overfitting, the degrees of freedom in the mapping can be controlled by choosing a suitable number of components. We propose a technique to combine PLS with GMMs, enabling the use of multiple local linear mappings. To further improve the perceptual quality of the mapping where rapid transitions between GMM components produce audible artefacts, we propose to low-pass filter the component posterior probabilities. The conducted experiments show that the proposed technique results in better subjective and objective quality than the baseline joint density GMM approach. In speech quality conversion preference tests, the proposed method achieved 67% preference score against the smoothed joint density GMM method and 84% preference score against the unsmoothed joint density GMM method. In objective tests the proposed method produced a lower Mel-cepstral distortion than the reference methods.

BackgroundHealthy eating interventions that use behavior change techniques such as self-monitoring and feedback have been associated with stronger effects. Mobile apps can make dietary self-monitoring easy with photography and potentially reach huge populations.ObjectiveThe aim of the study was to assess the factors related to sustained use of a free mobile app (“The Eatery”) that promotes healthy eating through photographic dietary self-monitoring and peer feedback.MethodsA retrospective analysis was conducted on the sample of 189,770 people who had downloaded the app and used it at least once between October 2011 and April 2012. Adherence was defined based on frequency and duration of self-monitoring. People who had taken more than one picture were classified as “Users” and people with one or no pictures as “Dropouts”. Users who had taken at least 10 pictures and used the app for at least one week were classified as “Actives”, Users with 2-9 pictures as “Semi-actives”, and Dropouts with one picture as “Non-actives”. The associations between adherence, registration time, dietary preferences, and peer feedback were examined. Changes in healthiness ratings over time were analyzed among Actives.ResultsOverall adherence was low—only 2.58% (4895/189,770) used the app actively. The day of week and time of day the app was initially used was associated with adherence, where 20.28% (5237/25,820) of Users had started using the app during the daytime on weekdays, in comparison to 15.34% (24,718/161,113) of Dropouts. Users with strict diets were more likely to be Active (14.31%, 900/6291) than those who had not defined any diet (3.99%, 742/18,590), said they ate everything (9.47%, 3040/32,090), or reported some other diet (11.85%, 213/1798) (χ2 3=826.6, P<.001). The average healthiness rating from peers for the first picture was higher for Active users (0.55) than for Semi-actives (0.52) or Non-actives (0.49) (F 2,58167=225.9, P<.001). Actives wrote more often a textual description for the first picture than Semi-actives or Non-actives (χ2 2=3515.1, P<.001). Feedback beyond ratings was relatively infrequent: 3.83% (15,247/398,228) of pictures received comments and 15.39% (61,299/398,228) received “likes” from other users. Actives were more likely to have at least one comment or one “like” for their pictures than Semi-actives or Non-actives (χ2 2=343.6, P<.001, and χ2 2=909.6, P<.001, respectively). Only 9.89% (481/4863) of Active users had a positive trend in their average healthiness ratings.ConclusionsMost people who tried out this free mobile app for dietary self-monitoring did not continue using it actively and those who did may already have been healthy eaters. Hence, the societal impact of such apps may remain small if they fail to reach those who would be most in need of dietary changes. Incorporating additional self-regulation techniques such as goal-setting and intention formation into the app could potentially increase user engagement and promote sustained use.

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Regular self-weighing is linked to successful weight loss and maintenance. However, an individual's self-weighing frequency typically varies over time. This study examined temporal associations between time differences of consecutive weight measurements and the corresponding weight changes by analysing longitudinal self-weighing data, including 2,838 weight observations from 40 individuals attending a health-promoting programme. The relationship between temporal weighing frequency and corresponding weight change was studied primarily using a linear mixed effects model. Weight change between consecutive weight measurements was associated with the corresponding time difference (β = 0.021% per day, p<0.001). Weight loss took place during periods of daily self-weighing, whereas breaks longer than one month posed a risk of weight gain. The findings emphasize that missing data in weight management studies with a weight-monitoring component may be associated with non-adherence to the weight loss programme and an early sign of weight gain.