In the last decades, encoder-decoders or autoencoders (AE) have received a great interest from researchers due to their capability to construct robust representations of documents in a low dimensional subspace. Nonetheless, autoencoders reveal little in way of spoken document internal structure by only considering words or topics contained in the document as an isolate basic element, and tend to overfit with small corpus of documents. Therefore, Quaternion Multi-layer Perceptrons (QMLP) have been introduced to capture such internal latent dependencies , whereas denoising autoencoders (DAE) are composed with different stochastic noises to better process small set of documents. This paper presents a novel autoencoder based on both hitherto-proposed DAE (to manage small corpus) and the QMLP (to consider internal latent structures) called "Quater-nion denoising encoder-decoder" (QDAE). Moreover, the paper defines an original angular Gaussian noise adapted to the speci-ficity of hyper-complex algebra. The experiments, conduced on a theme identification task of spoken dialogues from the DE-CODA framework, show that the QDAE obtains the promising gains of 3% and 1.5% compared to the standard real valued de-noising autoencoder and the QMLP respectively.