  In this work we aim to discover high quality speech features and linguistic
units directly from unlabeled speech data in a zero resource scenario. The
results are evaluated using the metrics and corpora proposed in the Zero
Resource Speech Challenge organized at Interspeech 2015. A Multi-layered
Acoustic Tokenizer (MAT) was proposed for automatic discovery of multiple sets
of acoustic tokens from the given corpus. Each acoustic token set is specified
by a set of hyperparameters that describe the model configuration. These sets
of acoustic tokens carry different characteristics fof the given corpus and the
language behind, thus can be mutually reinforced. The multiple sets of token
labels are then used as the targets of a Multi-target Deep Neural Network
(MDNN) trained on low-level acoustic features. Bottleneck features extracted
from the MDNN are then used as the feedback input to the MAT and the MDNN
itself in the next iteration. We call this iterative deep learning framework
the Multi-layered Acoustic Tokenizing Deep Neural Network (MAT-DNN), which
generates both high quality speech features for the Track 1 of the Challenge
and acoustic tokens for the Track 2 of the Challenge. In addition, we performed
extra experiments on the same corpora on the application of query-by-example
spoken term detection. The experimental results showed the iterative deep
learning framework of MAT-DNN improved the detection performance due to better
underlying speech features and acoustic tokens.
