Beyond words: Testing alignment among inter-organizational performance measures

Purpose: This paper seeks to measure in a quantitative way the degree of alignment among a set of performance measures between two organizations. Design/methodology/approach: This paper extends Venkatraman's test of coalignment to assess the alignment of a set of performance measures governing a contractual inter-organizational relationship. The authors applied the test and present coefficients of misalignment across three sets of measures: those used by a service provider involved in the research, those used by customers contracting the services, and those documented in 11 contracts studied. Findings: Results confirmed a high degree of alignment between target and actual operational performance in the contracts. The alignment of customers' financial objectives and contracts' operational metrics was low. Calculations show poor alignment between the objectives of the provider and the contribution received from the contracts. Research limitations/implications: Some limitations of the conclusions include the small sample of contracts used in the calculations. Further research should include not only actual contracts, but also failed ones. Practical implications: It is possible that misaligned goals, represented in misaligned performance measures, lead to tensions in intra-firm relationships. If these tensions are not addressed properly the relationship could be unstable or terminated prematurely. This method of measuring alignment could detect early potential dangers in intra-firm relationships. Originality/value: This paper extends Venkatraman's test of coalignment to assess the alignment of a set of performance measures governing a contractual inter-organizational relationship. Management researchers and business professionals may use this methodology when exploring degrees of alignment of performance measures in intra-functional and inter-firm relationships. © Emerald Group Publishing Limited.

Unsupervised intra-lingual and cross-lingual speaker adaptation for HMM-based speech synthesis using two-pass decision tree construction

Hidden Markov model (HMM)-based speech synthesis systems possess several advantages over concatenative synthesis systems. One such advantage is the relative ease with which HMM-based systems are adapted to speakers not present in the training dataset. Speaker adaptation methods used in the field of HMM-based automatic speech recognition (ASR) are adopted for this task. In the case of unsupervised speaker adaptation, previous work has used a supplementary set of acoustic models to estimate the transcription of the adaptation data. This paper first presents an approach to the unsupervised speaker adaptation task for HMM-based speech synthesis models which avoids the need for such supplementary acoustic models. This is achieved by defining a mapping between HMM-based synthesis models and ASR-style models, via a two-pass decision tree construction process. Second, it is shown that this mapping also enables unsupervised adaptation of HMM-based speech synthesis models without the need to perform linguistic analysis of the estimated transcription of the adaptation data. Third, this paper demonstrates how this technique lends itself to the task of unsupervised cross-lingual adaptation of HMM-based speech synthesis models, and explains the advantages of such an approach. Finally, listener evaluations reveal that the proposed unsupervised adaptation methods deliver performance approaching that of supervised adaptation.

Fabrication of carbon nanotubes on inter-digitated metal electrode for switchable nanophotonic devices

This paper reports the modeling and characterization of interdigitated rows of carbon nanotube electrodes used to address a liquid crystal media. Finite Element Method modeling of the nanotube arrays was performed to analyze the static electric Fields produced to Find suitable electrode geometry. A device was fabricated based on the simulation results and electro optics characteristics of the device are presented. This Finding has applications in the development of micron and submicron pixels, precise beem steering and nanotube based active back planes.

Improving statistical machine translation by classifying and generalizing inflected verb forms

This paper introduces a rule-based classification of single-word and compound verbs into a statistical machine translation approach. By substituting verb forms by the lemma of their head verb, the data sparseness problem caused by highly-inflected languages can be successfully addressed. On the other hand, the information of seen verb forms can be used to generate new translations for unseen verb forms. Translation results for an English to Spanish task are reported, producing a significant performance improvement.

An ngram-based statistical machine translation decoder

In this paper we describe MARIE, an Ngram-based statistical machine translation decoder. It is implemented using a beam search strategy, with distortion (or reordering) capabilities. The underlying translation model is based on an Ngram approach, extended to introduce reordering at the phrase level. The search graph structure is designed to perform very accurate comparisons, what allows for a high level of pruning, improving the decoder efficiency. We report several techniques for efficiently prune out the search space. The combinatory explosion of the search space derived from the search graph structure is reduced by limiting the number of reorderings a given translation is allowed to perform, and also the maximum distance a word (or a phrase) is allowed to be reordered. We finally report translation accuracy results on three different translation tasks.

Phrase linguistic classification and generalization for improving statistical machine translation

In this paper a method to incorporate linguistic information regarding single-word and compound verbs is proposed, as a first step towards an SMT model based on linguistically-classified phrases. By substituting these verb structures by the base form of the head verb, we achieve a better statistical word alignment performance, and are able to better estimate the translation model and generalize to unseen verb forms during translation. Preliminary experiments for the English - Spanish language pair are performed, and future research lines are detailed. © 2005 Association for Computational Linguistics.