Phone-to-word decoding through statistical machine translation and complementary system combination
Daniele Falavigna, Matteo Gerosa, Roberto Gretter, D. Giuliani · 2009
In this paper, phone-to-word transduction is first investigated by coupling a speech recognizer, generating for each speech segment a phone sequence or a phone confusion network, with the efficient decoder of confusion networks adopted by MOSES, a popular statistical machine translation toolkit. Then, system combination is investigated by combining the outputs of several conventional ASR systems with the output of a system embedding phone-to-word decoding through statistical machine translation. Experiments are carried out in the context of a large vocabulary speech recognition task consisting of transcription of speeches delivered in English during the European Parliament Plenary Sessions (EPPS). While only a marginal performance improvements is achieved in system combination experiments when the output of the phone-to-word transducer is included in the combination, partial results show a great potential for improvements.