Performance analysis of Distributed Speech Recognition over IP networks on the AURORA database
Performance analysis of Distributed Speech Recognition over IP networks on the AURORA database
复制标题
AURORA 数据库上 IP 网络分布式语音识别的性能分析
DOI：
10.1109/icassp.2002.5745489
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发表时间：
2002
期刊：
影响因子：
--
通讯作者：
Juan Carlos De Martin
中科院分区：
文献类型：
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作者：
D. Quercia;Laura Docío Fernández;C. García;L. Farinetti;Juan Carlos De Martin
我们目前的分布式语音识别的性能在模拟IP网络上运行的结果。运行在客户端节点的ETSI AURORA前端提取语音参数，打包并将其作为实时IP流量发送到基于连续密度隐马尔可夫模型的远程识别器。实验框架是ETSI STQ-AURORA项目数据库2.0。通过（1）随机损耗、（2）由吉尔伯特模型生成的损耗和（3）网络模拟来对IP网络上的传输的影响进行建模。结果表明，随机损失和中等突发性损失不会显着影响识别性能。相反，由于实时和Web流量在网络瓶颈上竞争而产生的强突发性数据包丢失可能对识别性能产生非常负面的影响，这表明在互联网上成功的DSR需要高水平的服务质量。
We present results on the performance of Distributed Speech Recognition operating over simulated IP networks. ETSI AURORA front-end running at client nodes extracts the speech parameters, packetizes and sends them as real-time IP traffic to a remote recognizer based on Continuous Density Hidden Markov Models. The experimental framework is the ETSI STQ-AURORA Project Database 2.0. The impact of transmission over IP networks is modeled by (1) random losses, (2) losses generated by a Gilbert model and (3) network simulations. Results show that random losses and moderately bursty losses do not significantly affect the recognition performance. Strongly bursty packet losses, as those generated by real-time and Web traffic competing over a network bottleneck, instead, can have a very negative impact on recognition performance, indicating that DSR over the Internet, to be successful, requires high levels of Quality of Service.