  Research on automatic speech recognition (ASR) systems for electrolaryngeal
speakers has been relatively unexplored due to small datasets. When training
data is lacking in ASR, a large-scale pretraining and fine tuning framework is
often sufficient to achieve high recognition rates; however, in
electrolaryngeal speech, the domain shift between the pretraining and
fine-tuning data is too large to overcome, limiting the maximum improvement of
recognition rates. To resolve this, we propose an intermediate fine-tuning step
that uses imperfect synthetic speech to close the domain shift gap between the
pretraining and target data. Despite the imperfect synthetic data, we show the
effectiveness of this on electrolaryngeal speech datasets, with improvements of
6.1% over the baseline that did not use imperfect synthetic speech. Results
show how the intermediate fine-tuning stage focuses on learning the high-level
inherent features of the imperfect synthetic data rather than the low-level
features such as intelligibility.
