Abstract
This paper presents some recent improvements in automatic transcription of Italian broadcast news obtained at ITCirst. A first preliminary activity was carried out in order to develop a suitable speech corpus for the Italian language. The resulting corpus, formed by recordings covering 30 hours of radio news, was exploited for developing a baseline system for transcription of broadcast news. The system performs in different stages: acoustic segmentation and classification, speaker clustering, acoustic model adaptation and speech decoding. Major recent advances allowing performance improvement concern with speech segmentation and clustering, acoustic modeling, acoustic model adaptation and the language model. The transcription system features a 14.3% word error rate on planned studio speech and 18.7% on the whole test set formed by recordings of radio broadcast news. When applied to a test set formed by recordings of television broadcast news, the system features 16.5% word error rate on planned studio speech and 23.2% by considering the whole test set.
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