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This paper describes the development of the CU-HTK Mandarin speech-to-text (STT) system and assesses its performance as part of a transcription-translation pipeline which converts broadcast Mandarin audio into English text. Recent improvements to the STT system are described and these give character error rate (CER) gains of 14.3% absolute for a broadcast conversation (BC) task and 5.1% absolute for a broadcast news (BN) task. The output of these STT systems is then post-processed, so that it consists of sentence-like segments, and translated into English text using a statistical machine translation (SMT) system. The performance of the transcription-translation pipeline is evaluated using the translation edit rate (TER) and BLEU metrics. It is shown that improving both the STT system and the post-STT segmentations can lower the TER scores by up to 5.3% absolute and increase the BLEU scores by up to 2.7% absolute.