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HCC-Small: Assistive Listening Devices and Voice Processing Platforms for the Deaf and Hard of Hearing

HCC-Small: Assistive Listening Devices and Voice Processing Platforms for the Deaf and Hard of Hearing
HCC-Small:针对聋哑人和听力障碍人士的助听设备和语音处理平台
批准号:
0812576
负责人:
Lawrence Saul
金额:
$14.9万
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2008
资助国家:
美国
项目状态:
已结题
起止时间:
2008-08-01 至 2011-07-31

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中文摘要
翻译
这项研究旨在开发辅助听力设备和语音处理平台,以促进与聋人和重听者的交流。为了实现这一目标,PI和他的团队都将采用语音处理和模式识别方面的现有技术,并为全新的人机交互形式奠定基础。将开发的具体设备和平台将从以下五个类别中挑选出来。(1)便于聋人用户参与双人或多人对话的对话口译器;假定对话发生在一张桌子上(在家里或办公室),其中包括用于语音识别的中央处理器和用于显示的监视器。(2)一种语音监测器,可以为重听用户提供有关其声音投射水平的即时、微妙的反馈;如果没有这种反馈,不适当的调制可能会在社交中带来尴尬,随着时间的推移导致退缩和孤立。(3)一种语音指南针,在多个说话人交谈时提供快速方向通知;这样的设备将帮助失聪的唇读者,否则他们无法跟踪不同说话者的转向(在这种设置中,唇读假定有能力知道往哪里看)。(4)一种音频事件检测器,可以识别用户环境中的显著单词、提示和声音;这样的设备可以在有人叫他们的名字或与工作相关的重要声音时通知用户听力损失,从而减少挫折感,提高就业能力。(5)一种可穿戴的发送器/接收器,它使用原始的语音识别技术,允许有语言和听力的用户与迟聋的爱人交流简单的话;这样的设备将补充剩余的听力、手势和唇读。布罗德影响:超过3500万美国人患有某种形式的听力损失。此外,据估计,其中五分之一的病例涉及严重或严重的听力损失。听力损失的发生率随着年龄的增长而增加:大约2%的18岁以下儿童受到听力损失的影响,但超过30%的65岁以上的成年人受到听力损失的影响。因此,受影响的成年人比例将随着婴儿潮一代的老龄化而增加。在家庭、社区和工作场所,迫切需要更好地整合听力损失患者。在聋人社区成员和听力正常人群之间经常存在极端的沟通障碍。即使在一个家庭里,这些障碍也会给失聪的家庭成员带来一种绝望的孤立感。在工作场所,聋人社区的成员和其他人一样身体健全,但沟通方面的困难给最佳就业和留住目前的职位制造了障碍。
英文摘要
This research aims to develop assistive listening devices and voice processing platforms that facilitate communication with the deaf and hard of hearing. To achieve this goal, the PI and his team will both adapt existing technologies in speech processing and pattern recognition, and lay the foundation for entirely novel forms of human-computer interaction. Specific devices and platforms that will be developed will be drawn from the following five categories. (1) A dialogue interpreter that facilitates the inclusion of deaf users in dual or multi-speaker conversations; the conversations are assumed to take place at a table (in the home or office), which accommodates CPUs for speech recognition and monitors for display. (2) A voice monitor that provides hard-of-hearing users with immediate, subtle feedback on the level of their vocal projections; without this feedback, inappropriate modulation can introduce awkwardness in social interactions, leading over time to withdrawal and isolation. (3) A voice compass that provides rapid directional notification during multi-speaker conversation; such a device would assist deaf lip-readers who cannot otherwise track the turns of different speakers (lip reading in this setting presumes the ability to know where to look). (4) An audio event detector that recognizes salient words, prompts, and sounds in the user's environment; such a device could notify users with hearing loss to someone calling their name or to important work-related sounds, thus reducing frustrations and boosting employability. (5) A wearable transmitter/receiver that uses primitive speech recognition to allow a user with speech and hearing to communicate simple words to a late deafened love one; such a device would complement residual hearing, gestures, and lip-reading.Broader Impacts: Over 35 million Americans have some form of hearing loss. Moreover, one-fifth of these cases are estimated to involve severe or profound hearing loss. Incidence of hearing loss increases with age: it affects roughly 2% of children under age 18 but over 30% of adults over age 65. Thus, the percentage of affected adults will grow with the aging of the baby-boomer generation. There is a tremendous need for better integration of people with hearing loss in the home, community, and workplace. Extreme barriers in communication often exist between members of the Deaf community and the hearing population. Even within a single household, these barriers can create a desperate sense of isolation for a family member who is deaf. In the workplace, the members of the Deaf community are as able-bodied as the rest of the population, but difficulties in communication create barriers to optimal employment and retention in current positions.
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Collaborative Research:EAGER:Deep Architectures for Speech and Audio Processing
  • 批准号:
    0957560
  • 项目类别:
    Standard Grant
  • 资助金额:
    $5.0万
  • 财政年份:
    2010
  • 负责人:
    Lawrence Saul
  • 依托单位:
CAREER: Statistical Methods for Dimensionality Reduction in Machine Learning
  • 批准号:
    0650074
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $0.0万
  • 财政年份:
    2006
  • 负责人:
    Lawrence Saul
  • 依托单位:
CAREER: Statistical Methods for Dimensionality Reduction in Machine Learning
  • 批准号:
    0238323
  • 项目类别:
    Continuing grant
  • 资助金额:
    $40.0万
  • 财政年份:
    2003
  • 负责人:
    Lawrence Saul
  • 依托单位:
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