Tanel Alumäe,Ottokar Tilk,Asadullah
Abstract URL: http://arxiv.org/abs/1901.03601v1
This paper describes the current TT\"U speech transcription system for
Estonian speech. The system is designed to handle semi-spontaneous speech, such
as broadcast conversations, lecture recordings and interviews recorded in
diverse acoustic conditions. The system is based on the Kaldi toolkit.
Multi-condition training using background noise profiles extracted
automatically from untranscribed data is used to improve the robustness of the
system. Out-of-vocabulary words are recovered using a phoneme n-gram based
decoding subgraph and a FST-based phoneme-to-grapheme model. The system
achieves a word error rate of 8.1% on a test set of broadcast conversations.
The system also performs punctuation recovery and speaker identification.
Speaker identification models are trained using a recently proposed weakly
supervised training method.