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Method of speaker adaptation for a hidden markov model based voice recognition system

机译:基于隐马尔可夫模型的语音识别系统的说话人自适应方法

摘要

Commercially available voice recognition systems are generally speaker-dependent, with the voice recognition system first being trained to the voice of the speaker before it can be used. A disadvantage with this method is that modified reference data has to be buffered and permanently saved in several steps when the speaker adaptation algorithm is executed, and thus requires a lot of memory space. This primarily negatively affects applications on devices with restricted processor power and limited memory space, such as mobile radio terminals for example. A method of speaker adaptation for a Hidden Markov Model based voice recognition system may address these issues. In the method, the memory space requirement and thus also the processor power required can be considerably reduced. This is achieved by using modified reference data in a speaker adaptation algorithm to adapt a new speaker to a reference speaker. The modified reference data is processed in compressed form.
机译:市售的语音识别系统通常取决于说话者,在使用语音识别系统之前,首先要对其语音进行训练。该方法的缺点是,当执行说话者自适应算法时,修改后的参考数据必须在多个步骤中进行缓冲和永久保存,因此需要大量的存储空间。这主要对处理器功率和存储空间有限的设备(例如移动无线终端)上的应用程序产生负面影响。用于基于隐马尔可夫模型的语音识别系统的说话者自适应方法可以解决这些问题。在该方法中,可以显着降低存储器空间需求以及因此所需的处理器功率。这是通过在说话人适应算法中使用修改后的参考数据来使新说话人适应参考说话人来实现的。修改后的参考数据以压缩形式处理。

著录项

  • 公开/公告号US8041567B2

    专利类型

  • 公开/公告日2011-10-18

    原文格式PDF

  • 申请/专利权人 SERGEY ASTROV;JOSEF BAUER;

    申请/专利号US20050231940

  • 发明设计人 JOSEF BAUER;SERGEY ASTROV;

    申请日2005-09-22

  • 分类号G10L15/14;

  • 国家 US

  • 入库时间 2022-08-21 18:12:53

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