  Detecting spoofing attempts of automatic speaker verification (ASV) systems
is challenging, especially when using only one modeling approach. For
robustness, we use both deep neural networks and traditional machine learning
models and combine them as ensemble models through logistic regression. They
are trained to detect logical access (LA) and physical access (PA) attacks on
the dataset released as part of the ASV Spoofing and Countermeasures Challenge
2019. We propose dataset partitions that ensure different attack types are
present during training and validation to improve system robustness. Our
ensemble model outperforms all our single models and the baselines from the
challenge for both attack types. We investigate why some models on the PA
dataset strongly outperform others and find that spoofed recordings in the
dataset tend to have longer silences at the end than genuine ones. By removing
them, the PA task becomes much more challenging, with the tandem detection cost
function (t-DCF) of our best single model rising from 0.1672 to 0.5018 and
equal error rate (EER) increasing from 5.98% to 19.8% on the development set.
