Performance analysis of Distributed Speech Recognition over IP networks on the AURORA database

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.