  This paper reports the LEAP submission to the CHiME-6 challenge. The CHiME-6
Automatic Speech Recognition (ASR) challenge Track 1 involved the recognition
of speech in noisy and reverberant acoustic conditions in home environments
with multiple-party interactions. For the challenge submission, the LEAP system
used extensive data augmentation and a factorized time-delay neural network
(TDNN) architecture. We also explored a neural architecture that interleaved
the TDNN layers with LSTM layers. The submitted system improved the Kaldi
recipe by 2% in terms of relative word-error-rate improvements.
