  Recognizer Output Voting Error Reduction (ROVER) has been widely used for
system combination in automatic speech recognition (ASR). In order to select
the most appropriate words to insert at each position in the output
transcriptions, some ROVER extensions rely on critical information such as
confidence scores and other ASR decoder features. This information, which is
not always available, highly depends on the decoding process and sometimes
tends to over estimate the real quality of the recognized words. In this paper
we propose a novel variant of ROVER that takes advantage of ASR quality
estimation (QE) for ranking the transcriptions at "segment level" instead of:
i) relying on confidence scores, or ii) feeding ROVER with randomly ordered
hypotheses. We first introduce an effective set of features to compensate for
the absence of ASR decoder information. Then, we apply QE techniques to perform
accurate hypothesis ranking at segment-level before starting the fusion
process. The evaluation is carried out on two different tasks, in which we
respectively combine hypotheses coming from independent ASR systems and
multi-microphone recordings. In both tasks, it is assumed that the ASR decoder
information is not available. The proposed approach significantly outperforms
standard ROVER and it is competitive with two strong oracles that e xploit
prior knowledge about the real quality of the hypotheses to be combined.
Compared to standard ROVER, the abs olute WER improvements in the two
evaluation scenarios range from 0.5% to 7.3%.
