Initially introduced in the late 1960s and early 1970s, dynamic programming algorithms have become increasingly popular in automatic speech recognition. There are two reasons why this has occurred: First, the dynamic programming strategy can be combined with avery efficient and practical pruning strategy so that very large search spaces can be handled. Second, the dynamic programming strategy has turned out to be extremely flexible in adapting to new requirements. Examples of such requirements are the lexical tree organization of the pronunciation lexicon and the generation of a word graph instead of the single best sentence. In this paper, we attempt to systematically review the use of dynamic programming search strategies for small-vocabulary and large-vocabulary continuous speech recognition. The following methods are described in detail: search using a linear lexicon, search using a lexical tree, language-model look-ahead and word graph generation.

event in one or more data collections. In the absence of a shared, unique key, record linkage involves the comparison of ensembles of partially-identifying, non-unique data items between pairs of records. Data items with variable formats, such as names and addresses, need to be transformed and normalised in order to validly carry out these comparisons. Traditionally, deterministic rule-based data processing systems have been used to carry out this pre-processing, which is commonly referred to as "standardisation". This paper describes an alternative approach to standardisation, using a combination of lexicon-based tokenisation and probabilistic hidden Markov models (HMMs).

event in one or more data collections. In the absence of a shared, unique key, record linkage involves the comparison of ensembles of partially-identifying, non-unique data items between pairs of records. Data items with variable formats, such as names and addresses, need to be transformed and normalised in order to validly carry out these comparisons. Traditionally, deterministic rule-based data processing systems have been used to carry out this pre-processing, which is commonly referred to as "standardisation". This paper describes an alternative approach to standardisation, using a combination of lexicon-based tokenisation and probabilistic hidden Markov models (HMMs).

Demarcation in human interaction is through two channels: one transmits explicit messages; the other transmits implicit messages about the speakers themselves knowingly or unknowingly. Both linguistics and technology have invested enormous effort in trying to understand the first (explicit) channel, but the second (implicit) is not as well understood. First, building an emotion detection system makes it possible to assess the extent to which theoretical proposals explain people's everyday competence in understanding emotion. Second, model building enforces coherence. It is true that emotions play an important role in the making of speech. The deduction of emotions from speech is of recent origin and it is the primary focus of this research paper.

Demarcation in human interaction is through two channels: one transmits explicit messages; the other transmits implicit messages about the speakers themselves knowingly or unknowingly. Both linguistics and technology have invested enormous effort in trying to understand the first (explicit) channel, but the second (implicit) is not as well understood. First, building an emotion detection system makes it possible to assess the extent to which theoretical proposals explain people's everyday competence in understanding emotion. Second, model building enforces coherence. It is true that emotions play an important role in the making of speech. The deduction of emotions from speech is of recent origin and it is the primary focus of this research paper.