The prediction of the linguistic origin of surnames is a basic functionality required in the design of high-quality multilanguage speech synthesizers. The assignment of a given string representing a surname to a specific language is typically based on a set of rules which can hardly be written in an explicit form. The approach we propose faces this problem combining a rule-based system with a module based on evidential reasoning and a module based on neural networks. The resulting hybrid system combines the different sources of information, merging both knowledge from experts on linguistics and knowledge automatically acquired using learning from examples. The system has been validated on a large database containing surnames belonging to four different languages, showing its effectiveness for real-world applications.

This paper is a case study of user involvement in the requirements specification for project ISLE: Interactive Spoken Language Education. Developers of Spoken Language Dialogue Systems should involve users from the outset, particularly if the aim is to develop novel solutions for a generic target application area or market. As well as target end-users, SLDS developers should identify and consult ‘meta-level’ domain experts with expertise in human-to-human dialogue in the target domain. In our case, English language teachers and publishers provided generic knowledge of learners' dialogue preferences; other applications have analogous domain language experts. These domain language experts can help to pin down a domain-specific sublanguage which fits the constraints of current speech recognition technology: linguistically-naive end-users may expect unconstrained conversational English, but in practice, dialogue interactions have to be constrained in vocabulary and syntax. User consultation also highlighted a need to consider how to integrate speech input and output with other modes of interaction and processing; in our case the input speech signal is processed by speech recogniser, stress and mispronunciation detectors, and output responses are text and graphics as well as speech. This suggests a need to revisit the definition of ‘dialogue’: other SLDS developers should also consider the merits of multimodality as an adjunct to pure spoken language dialogue, particularly given that current systems are not capable of accurately handling unconstrained English.

This paper describes an application to the prediction of the linguistic origin of surnames. The problem can be stated as follows: "Given an input string representing a surname, decide which language the surname belongs to". We present an approach that integrates methodologies from rule-based systems, evidential reasoning, and neural networks. Our hybrid solution maximizes the used information and allows one to deal with aspects of the problem that could have not been solved otherwise. In fact, our predictor exploits both knowledge from experts on languages (by means of a rule-based system) and knowledge automatically acquired by examples (through statistical analysis and a neural network).