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The vector representations of fixed dimensionality for words (in text)offered by Word2Vec have been shown to be very useful in many applicationscenarios, in particular due to the semantic information they carry. This paperproposes a parallel version, the Audio Word2Vec. It offers the vectorrepresentations of fixed dimensionality for variable-length audio segments.These vector representations are shown to describe the sequential phoneticstructures of the audio segments to a good degree, with very attractive realworld applications such as query-by-example Spoken Term Detection (STD). Inthis STD application, the proposed approach significantly outperformed theconventional Dynamic Time Warping (DTW) based approaches at significantly lowercomputation requirements. We propose unsupervised learning of Audio Word2Vecfrom 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 vectorrepresentation of fixed dimensionality, and the second RNN (decoder) maps therepresentation back to the input audio sequence. The two RNNs are jointlytrained by minimizing the reconstruction error. Denoising Sequence-to-sequenceAutoencoder (DSA) is furthered proposed offering more robust learning.

Audio Word2Vec: Unsupervised Learning of Audio Segment Representations using Sequence-to-sequence Autoencoder