The Management of Context-Sensitive Features: A Review of Strategies <s> Applying the Framework to the Research Literature <s> A method for speaker normalization and adaption using connectionist networks is developed. A speaker-specific linear transformation of observations of the speech signal is computed using second-order network units. Classification is accomplished by a multilayer feedforward network that operates on the normalized speech data. The network is adapted for a new talker by modifying the transformation parameters while leaving the classifier fixed. This is accomplished by backpropagating classification error through the classifier to the second-order transformation units. This method was evaluated for the classification of ten vowels for 76 speakers using the first two formant values of the Peterson-Barney data. The results suggest that rapid speaker adaptation resulting in high classification accuracy can be accomplished by this method. > <s> BIB001 </s> The Management of Context-Sensitive Features: A Review of Strategies <s> Applying the Framework to the Research Literature <s> The disclosure of this specification illustrates a GAP FILLER, designed to be easily and quickly removed from the gap between poured concrete sections, after the concrete sets. The individual plastic-foam slats, held together and attached to each other, are packaged in a watertight polyethylene bag, and are positioned against an existng concrete wall, ready to receive the second concrete pour. The zip-out filler will form the expansion joint between the two concrete pours. Removal of the filler is easily accomplished because of the inter-connected slats. <s> BIB002
The preceding sections of this paper have sketched a framework for categorizing strategies for learning in context-sensitive domains. We will now apply this scheme to a sample of the research literature. Table 2 shows how some of the papers fit into our structure. All of the papers we have read so far appear to be consistent with the framework. In Table 2 , context management refers to the five heuristics for managing context-sensitive features that are discussed in Section 2; context recovery refers to the method for recovering lost contextual features, as discussed in Section 3. Explicit means that the contextual features are explicitly present in the datasets. Implicit means that the contextual features were not recorded in the data, so the learning algorithm must attempt to recover lost contextual information. The implicit contextual information may be recovered either by clustering the data or exploiting the temporal sequence of the examples.  BIB001 Selection Implicittemporal sequence BIB002 Selection Explicit