Surface astigmatism correction using segmented freeform surfaces for a progressive addition lens.

The undesired distribution of irregular surface astigmatism (SA) on the freeform surface has been the major concern of progressive addition lens (PAL) design. Herein, we proposed a segmented freeform surface (SFS) construction method, which relies on the lines of curvature to rule the surface segmentation and then eliminates the difference between principal curvatures to correct the SA. Based on ray tracing and numerical simulation results, the SFS-PAL design has superior performance in image quality within a dynamic field of view over the conventional freeform PAL. To verify the feasibility and the real performance of the new design, we used the diamond turning method with a fast tool servo to realize the rapid prototyping, and then used injection molding for the mass production of the high-quality SFS-PALs.

Effect of the division between early and late reflections on intelligibility of ideal binary-masked speech.

The ideal binary mask (IBM) that was originally defined in anechoic conditions has been found to yield substantial improvements in speech intelligibility in noise. The IBM has recently been extended to reverberant conditions where the direct sound and early reflections of target speech are regarded as the desired signal. It is of great interest to know how the division between early and late reflections impacts on the intelligibility of the IBM-processed noisy reverberant speech. In this present study, the division between early and late reflections in three rooms was first determined by four typical estimation approaches and then used to compute the IBMs in reverberant conditions. The IBMs were then applied to the noisy reverberant mixture signal for segregating the desired signal, and the segregated signal was further presented to normal-hearing listeners for word recognition. Results showed that the IBMs with different divisions between early and late reflections provided substantial improvements in speech intelligibility over the unprocessed mixture signals in all conditions tested, and there were small, but statistically significant, differences in speech intelligibility between the different IBMs in some conditions tested.

Investigation of objective measures for intelligibility prediction of noise-reduced speech for Chinese, Japanese, and English.

Many objective measures have been reported to predict speech intelligibility in noise, most of which were designed and evaluated with English speech corpora. Given the different perceptual cues used by native listeners of different languages, examining whether there is any language effect when the same objective measure is used to predict speech intelligibility in different languages is of great interest, particularly when non-linear noise-reduction processing is involved. In the present study, an extensive evaluation is taken of objective measures for speech intelligibility prediction of noisy speech processed by noise-reduction algorithms in Chinese, Japanese, and English. Of all the objective measures tested, the short-time objective intelligibility (STOI) measure produced the most accurate results in speech intelligibility prediction for Chinese, while the normalized covariance metric (NCM) and middle-level coherence speech intelligibility index ( CSIIm) incorporating the signal-dependent band-importance functions (BIFs) produced the most accurate results for Japanese and English, respectively. The objective measures that performed best in predicting the effect of non-linear noise-reduction processing in speech intelligibility were found to be the BIF-modified NCM measure for Chinese, the STOI measure for Japanese, and the BIF-modified CSIIm measure for English. Most of the objective measures examined performed differently even under the same conditions for different languages.