Mandarin speech perception in combined electric and acoustic stimulation.

For deaf individuals with residual low-frequency acoustic hearing, combined use of a cochlear implant (CI) and hearing aid (HA) typically provides better speech understanding than with either device alone. Because of coarse spectral resolution, CIs do not provide fundamental frequency (F0) information that contributes to understanding of tonal languages such as Mandarin Chinese. The HA can provide good representation of F0 and, depending on the range of aided acoustic hearing, first and second formant (F1 and F2) information. In this study, Mandarin tone, vowel, and consonant recognition in quiet and noise was measured in 12 adult Mandarin-speaking bimodal listeners with the CI-only and with the CI+HA. Tone recognition was significantly better with the CI+HA in noise, but not in quiet. Vowel recognition was significantly better with the CI+HA in quiet, but not in noise. There was no significant difference in consonant recognition between the CI-only and the CI+HA in quiet or in noise. There was a wide range in bimodal benefit, with improvements often greater than 20 percentage points in some tests and conditions. The bimodal benefit was compared to CI subjects' HA-aided pure-tone average (PTA) thresholds between 250 and 2000 Hz; subjects were divided into two groups: "better" PTA (<50 dB HL) or "poorer" PTA (>50 dB HL). The bimodal benefit differed significantly between groups only for consonant recognition. The bimodal benefit for tone recognition in quiet was significantly correlated with CI experience, suggesting that bimodal CI users learn to better combine low-frequency spectro-temporal information from acoustic hearing with temporal envelope information from electric hearing. Given the small number of subjects in this study (n = 12), further research with Chinese bimodal listeners may provide more information regarding the contribution of acoustic and electric hearing to tonal language perception

DCRNN: A Deep Cross approach based on RNN for Partial Parameter Sharing in Multi-task Learning

In recent years, DL has developed rapidly, and personalized services are exploring using DL algorithms to improve the performance of the recommendation system. For personalized services, a successful recommendation consists of two parts: attracting users to click the item and users being willing to consume the item. If both tasks need to be predicted at the same time, traditional recommendation systems generally train two independent models. This approach is cumbersome and does not effectively model the relationship between the two subtasks of "click-consumption". Therefore, in order to improve the success rate of recommendation and reduce computational costs, researchers are trying to model multi-task learning. At present, existing multi-task learning models generally adopt hard parameter sharing or soft parameter sharing architecture, but these two architectures each have certain problems. Therefore, in this work, we propose a novel recommendation model based on real recommendation scenarios, Deep Cross network based on RNN for partial parameter sharing (DCRNN). The model has three innovations: 1) It adopts the idea of cross network and uses RNN network to cross-process the features, thereby effectively improves the expressive ability of the model; 2) It innovatively proposes the structure of partial parameter sharing; 3) It can effectively capture the potential correlation between different tasks to optimize the efficiency and methods for learning different tasks.Comment: Work done while the first author was an algorithm engineer at Xiaomi In