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Marius C Silaghi

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AAAI Conference 2007 Conference Paper

Dynamic DFS Tree in ADOPT-ing

  • Marius C Silaghi

Several distributed constraint reasoning algorithms employ Depth First Search (DFS) trees on the constraint graph that spans involved agents. In this article we show that it is possible to dynamically detect a minimal DFS tree, compatible with the current order on agents, during the distributed constraint reasoning process of the ADOPT algorithm. This also allows for shorter DFS trees during the initial steps of the algorithm, while some constraints did not yet prove useful given visited combinations of assignments. Earlier distributed algorithms for finding spanning trees on agents did not look to maintain compatibility with an order already used. We also show that announcing a nogood to a single optional agent is bringing significant improvements in the total number of messages. The dynamic detection of the DFS tree brings improvements in simulated time.

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AAAI Conference 2005 Conference Paper

Spotting Subsequences Matching an HMM Using the Average Observation Probability Criteria with Application to Keyword Spotting

  • Marius C Silaghi

This paper addresses the problem of detecting keywords in unconstrained speech. The proposed algorithms search for the speech segment maximizing the average observation probability1 along the most likely path in the hypothesized keyword model. As known, this approach (sometimes referred to as sliding model method) requires a relaxation of the begin/endpoints of the Viterbi matching, as well as a time normalization of the resulting score. This makes solutions complex (i. e. , LN2 2 basic operations for keyword HMM models with L states and utterances with N frames). We present here two alternative (quite simple and efficient) solutions to this problem. a) First we provide a method that finds the optimal segmentation according to the criteria of maximizing the average observation probability. It uses Dynamic Programming as a step, but does not require scoring for all possible begin/endpoints. While the worst case remains O(LN2 ), this technique converged in at most 3(L+2)N basic operations in each experiment for two very different applications. b) The second proposed algorithm does not provide a segmentation but can be used for the decision problem of whether the utterance should be classified as containing the keyword or not (provided a predefined threshold on the acceptable average observation probability). This allows the algorithm to be even faster, with fix cost of (L+2)N.

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