  The vector representations of fixed dimensionality for words (in text)
offered by Word2Vec have been shown to be very useful in many application
scenarios, in particular due to the semantic information they carry. This paper
proposes a parallel version, the Audio Word2Vec. It offers the vector
representations of fixed dimensionality for variable-length audio segments.
These vector representations are shown to describe the sequential phonetic
structures of the audio segments to a good degree, with very attractive real
world applications such as query-by-example Spoken Term Detection (STD). In
this STD application, the proposed approach significantly outperformed the
conventional Dynamic Time Warping (DTW) based approaches at significantly lower
computation requirements. We propose unsupervised learning of Audio Word2Vec
from audio data without human annotation using Sequence-to-sequence Audoencoder
(SA). SA consists of two RNNs equipped with Long Short-Term Memory (LSTM)
units: the first RNN (encoder) maps the input audio sequence into a vector
representation of fixed dimensionality, and the second RNN (decoder) maps the
representation back to the input audio sequence. The two RNNs are jointly
trained by minimizing the reconstruction error. Denoising Sequence-to-sequence
Autoencoder (DSA) is furthered proposed offering more robust learning.
