Improving proper name recognition by means of automatically learned pronunciation variants

This paper introduces a novel lexical modeling approach that aims to improve large vocabulary proper name recognition for native and non-native speakers. The method uses one or more so-called phoneme-to-phoneme (P2P) converters to add useful pronunciation variants to a baseline lexicon. Each P2P converter is a stochastic automaton that applies context-dependent transformation rules to a baseline transcription that is generated by a standard grapheme-to-phoneme (G2P) converter. The paper focuses on the inclusion of different types of features to describe the rule context – ranging from the identities of neighboring phonemes to morphological and even semantic features such as the language of origin of the name – and on the development and assessment of methods that can cope with cross-lingual issues. Another aim is to ensure that the proposed solutions are applicable to new names (not seen during system development) and useful in the hands of product developers with good knowledge of their application domain but little expertise in automatic speech recognition (ASR) and speech corpus acquisition. The proposed method was evaluated on person name and geographical name recognition, two economically interesting domains in which non-native speakers as well as non-native names occur very frequently. For the recognition experiments a state-of-the-art commercial ASR engine was employed. The experimental results demonstrate that significant improvements of the recognition accuracy can be achieved: large gains (up to 40% relative) in case prior knowledge of the speaker tongue and the name origin is available, and still significant gains in case no such prior information is available.