Multi-domain spoken dialogue systems with high degree of intelligence and domain extensibility have long been desired but difficult to achieve. When the user freely surfs among different topics during the dialogue, it will be very difficult for the system to control the switching of the topics and domains while keeping the dialogue consistent, and decide when and how to take the initiative. This paper presents a distributed agent architecture for multi-domain spoken dialogue systems with high domain extensibility and intelligence. Under this architecture, different spoken dialogue agents (SDA's) handling different domains can be developed independently, and then smoothly cooperate with one another to achieve the user's multiple goals, while a user interface agent (UIA) can access the correct spoken dialogue agent through a domain switching protocol, and carry over the dialogue state and history so as to keep the knowledge processed coherently across different domains.

This paper describes the application of two attractive categories of topic modeling techniques to the problem of spoken document retrieval (SDR), viz. document topic model (DTM) and word topic model (WTM). Apart from using the conventional unsupervised training strategy, we explore a supervised training strategy for estimating these topic models, imagining a scenario that user query logs along with click-through information of relevant documents can be utilized to build an SDR system. This attempt has the potential to associate relevant documents with queries even if they do not share any of the query words, thereby improving on retrieval quality over the baseline system. Likewise, we also study a novel use of pseudo-supervised training to associate relevant documents with queries through a pseudo-feedback procedure. Moreover, in order to lessen SDR performance degradation caused by imperfect speech recognition, we investigate leveraging different levels of index features for topic modeling, including words, syllable-level units, and their combination. We provide a series of experiments conducted on the TDT (TDT-2 and TDT-3) Chinese SDR collections. The empirical results show that the methods deduced from our proposed modeling framework are very effective when compared with a few existing retrieval approaches.

In this paper, we demonstrate a 10.66 Gb/s bidirectional TDM over long-reach WDM hybrid PON supported by distributed Raman amplification, and the power budget margin is measured to be 15 dB for downstream transmission and 12 dB for upstream transmission, with dual Raman pump power of 300 mW.