A Study of Distributed Speaker Recognition Methods

在本研究中，我们主要研究一种分布式说话人识别方法（DSR）。DSR将识别的结构和计算组件分为两个组件-终端上的前端处理和服务器上的说话人识别引擎。DSR最重要的优点是它可以使用一个高频分量，在这个高频分量中，特定于说话人的信息被揭示出来。另一方面，DSR必须压缩发送数据以建立用于传输的较低比特率。为了实现高精度和低比特率，我们开发了以下技术。1）实时偏差去除方法，提高了对卷积噪声的鲁棒性，2）提出了一种非参数说话人识别方法，该方法由直方图-这些方法在低比特率条件下建立了与麦克风输入相当的高精度等效，4.8kbps，这是欧洲电信标准协会（ETSI）标准分布式语音识别系统推荐的比特率。我们还提出了以下技术，这些技术不仅可以用于DSR，而且可以用于传统的电话网络。一种相关说话人识别方法，其中在客户端对语音信号进行分段，并选择用于说话人识别的最有效音素发送到4）使用语音合成的编解码语音的快速声学模型自适应技术。5）使用基于MFT的语音识别和基于HMM的语音合成的分组丢失隐藏算法。此外，我们在两年多的时间里每周记录四个人的声音，以调查声音特征的变化。现在，我们正在探索使用这些数据表征说话者的基本语音属性。

In this research, we focused on a Distributed Speaker Recognition Method (DSR). DSR separates the structural and computational components of recognition into two components - the front-end processing on the terminal and the speaker recognition engine on the server. The most important advantage of DSR is that it can use a high frequency component in which speaker-specific information is revealed. On the other hand, DSR has to compress the sending data to establish a lower bit rate for transmission. In order to achieve both high accuracy and low bit rate, we have developed the following techniques.1)A Real-time bias removal method that improves the robustness against convolutional noises, which increases quantization distortion and recognition error.2)A Nonparametric speaker recognition method that consists of a histogram-based speaker model and Earth Mover's Distance.These proposed methods have established a high accuracy equivalent to microphone input under the condition of a low bit rate, 4.8kbps, which is the bit rate recommended as the European Telecommunication Standards Institute (ETSI) Standard Distributed Speech Recognition System.We also proposed the following techniques that can be used not only in DSR but in conventional telephone networks.3)A Phoneme-dependent speaker recognition method in which speech signals are segmented at the client and the most effective phonemes for speaker recognition are selected to be sent to the server.4)A Rapid acoustic model adaptation technique for codec speech using speech synthesis.5)Packet-loss concealment algorithms using MFT-based speech recognition and HMM-based speech synthesis.Furthermore, we have been recording four peoples' voices every week over two years to investigate change in voice characteristics. Now, we are exploring essential voice attributes that characterize a speaker using these data.