Dealing with cross-lingual aspects in spoken name recognition

Abstract

The development of an automatic speech recognizer (ASR) that can accurately recognize spoken names belonging to a large lexicon, is still a big challenge. One of the bottlenecks is that many names contain elements of a foreign language origin, and native speakers can adopt very different pronunciations of these elements, ranging from completely nativized to completelyforeignized pronunciations. In this paper we further develop a recently proposed method for improving the recognition of foreign proper names spoken by native speakers. The main idea is to combine the standard acoustic model scores with scores emerging from a phonologically inspired back-off model that was trained on native speech only. This means that the proposed method does not require the development of any foreign phoneme models on foreign speech data. By applying our method on a baseline Dutch recognizer (comprising Dutch acoustic models) we could reduce the name error rate for French and English names by a considerable amount

Text-based language identification of multilingual names

“…From a human perspective, different speakers tend to pronounce unfamiliar names taking factors such as orthographic irregularity and possible loanword status into account. Specifically, people tend to mimic pronunciations based on what they believe the origin of the name to be, or by replacing unfamiliar phoneme(s) with those they believe are the approximate in their mother tongue [2], [3].…”