Speech produced in the presence of noise (Lombard speech) is typically more intelligible than speech produced in quiet (plain speech) when presented at the same signal-to-noise ratio, but the factors responsible for the Lombard intelligibility benefit remain poorly understood. Previous studies have demonstrated a clear effect of spectral differences between the two speech styles and a lack of effect of fundamental frequency differences. The current study investigates a possible role for durational differences alongside spectral changes. Listeners identified keywords in sentences manipulated to possess either durational or spectral characteristics of plain or Lombard speech. Durational modifications were produced using linear or nonlinear time warping, while spectral changes were applied at the global utterance level or to individual time frames. Modifications were made to both plain and Lombard speech. No beneficial effects of durational increases were observed in any condition. Lombard sentences spoken at a speech rate substantially slower than their plain counterparts also failed to reveal a durational benefit. Spectral changes to plain speech resulted in large intelligibility gains, although not to the level of Lombard speech. These outcomes suggest that the durational increases seen in Lombard speech have little or no role in the Lombard intelligibility benefit.

No abstract

No abstract

No abstract

In this paper, we propose spatio-temporal silhouette representations, called silhouette energy image (SEI) and silhouette history image (SHI) to characterize motion and shape properties for recognition of human movements such as human actions, activities in daily life. The SEI and SHI are constructed by using the silhouette image sequence of an action. The span or difference of the end time and start time is used to make the SHI. For addressing the human shape variability, we used the variation of the anthropometry of the person. We extract the features based on geometric shape moments. We tested our approach successfully in the indoor and outdoor environment. Our experimental results show that the proposed method of human action recognition is robust, flexible and efficient.