Paper Info
Title
Quaternion Denoising Encoder-Decoder For Theme Identification Of Telephone Conversations
Abstract
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, Quatemion 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 "Quaternion denoising encoder-decoder" (QDAE). Moreover, the paper defines an original angular Gaussian noise adapted to the specificity 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.
Year
DOI
Venue
2017
10.21437/Interspeech.2017-1029
18TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2017), VOLS 1-6: SITUATED INTERACTION
Keywords
Field
DocType
Spoken language understanding, Neural networks, Quaternion algebra, Denoising encoder-decoder neural networks
Noise reduction,Computer vision,Encoder decoder,Computer science,Quaternion,Speech recognition,Artificial intelligence
Conference
ISSN
Citations 
PageRank 
2308-457X
2
0.38
References 
Authors
0
3
Name
Order
Citations
PageRank
Titouan Parcollet1169.23
Mohamed Morchid28422.79
georges linar es313629.55