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    • 1. 发明申请
    • PRIVACY-SENSITIVE SPEECH MODEL CREATION VIA AGGREGATION OF MULTIPLE USER MODELS
    • 通过多种用户模型的融合进行隐私认知语音模式创建
    • US20140129226A1
    • 2014-05-08
    • US13668662
    • 2012-11-05
    • Antonio R. LeePetr NovakPeder A. OlsenVaibhava Goel
    • Antonio R. LeePetr NovakPeder A. OlsenVaibhava Goel
    • G10L15/04
    • G10L15/065G06F21/6245G06F21/78G10L15/04H04L63/0407
    • Techniques disclosed herein include systems and methods for privacy-sensitive training data collection for updating acoustic models of speech recognition systems. In one embodiment, the system locally creates adaptation data from raw audio data. Such adaptation can include derived statistics and/or acoustic model update parameters. The derived statistics and/or updated acoustic model data can then be sent to a speech recognition server or third-party entity. Since the audio data and transcriptions are already processed, the statistics or acoustic model data is devoid of any information that could be human-readable or machine readable such as to enable reconstruction of audio data. Thus, such converted data sent to a server does not include personal or confidential information. Third-party servers can then continually update speech models without storing personal and confidential utterances of users.
    • 本文公开的技术包括用于更新语音识别系统的声学模型的用于隐私敏感的训练数据收集的系统和方法。 在一个实施例中,系统从原始音频数据本地创建适配数据。 这种适应可以包括导出的统计和/或声学模型更新参数。 导出的统计和/或更新的声学模型数据随后可被发送到语音识别服务器或第三方实体。 由于已经处理了音频数据和转录,所以统计数据或声学模型数据没有任何可能是人可读或机器可读的信息,例如能够重建音频数据。 因此,发送到服务器的转换数据不包括个人或机密信息。 然后,第三方服务器可以不间断地更新语音模型,而不会存储用户的个人和机密话语。