  In this paper we aim to automatically discover high quality frame-level
speech features and acoustic tokens directly from unlabeled speech data. A
Multi-granular 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 describing the model configuration.
These different sets of acoustic tokens carry different characteristics for 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 frame-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. The multi-granular acoustic token
sets and the frame-level speech features can be iteratively optimized in the
iterative deep learning framework. We call this framework the Multi-granular
Acoustic Tokenizing Deep Neural Network (MATDNN). The results were evaluated
using the metrics and corpora defined in the Zero Resource Speech Challenge
organized at Interspeech 2015, and improved performance was obtained with a set
of experiments of query-by-example spoken term detection on the same corpora.
Visualization for the discovered tokens against the English phonemes was also
shown.
