Unsupervised Phoneme and Word Discovery from Multiple Speakers using Double Articulation Analyzer and Neural Network with Parametric Bias

This paper describes a new unsupervised machine learning method for simultaneous phoneme and word discovery from multiple speakers. Human infants can acquire knowledge of phonemes and words from interactions with his/her mother as well as with others surrounding him/her. From a computational perspective, phoneme and word discovery from multiple speakers is a more challenging problem than that from one speaker because the speech signals from different speakers exhibit different acoustic features. This paper proposes an unsupervised phoneme and word discovery method that simultaneously uses nonparametric Bayesian double articulation analyzer (NPB-DAA) and deep sparse autoencoder with parametric bias in hidden layer (DSAE-PBHL). We assume that an infant can recognize and distinguish speakers based on certain other features, e.g., visual face recognition. DSAE-PBHL is aimed to be able to subtract speaker-dependent acoustic features and extract speaker-independent features. An experiment demonstrated that DSAE-PBHL can subtract distributed representations of acoustic signals, enabling extraction based on the types of phonemes rather than on the speakers. Another experiment demonstrated that a combination of NPB-DAA and DSAE-PB outperformed the available methods in phoneme and word discovery tasks involving speech signals with Japanese vowel sequences from multiple speakers.Comment: 21 pages. Submitte

Double Articulation Analyzer with Prosody for Unsupervised Word and Phoneme Discovery

Infants acquire words and phonemes from unsegmented speech signals using segmentation cues, such as distributional, prosodic, and co-occurrence cues. Many pre-existing computational models that represent the process tend to focus on distributional or prosodic cues. This paper proposes a nonparametric Bayesian probabilistic generative model called the prosodic hierarchical Dirichlet process-hidden language model (Prosodic HDP-HLM). Prosodic HDP-HLM, an extension of HDP-HLM, considers both prosodic and distributional cues within a single integrative generative model. We conducted three experiments on different types of datasets, and demonstrate the validity of the proposed method. The results show that the Prosodic DAA successfully uses prosodic cues and outperforms a method that solely uses distributional cues. The main contributions of this study are as follows: 1) We develop a probabilistic generative model for time series data including prosody that potentially has a double articulation structure; 2) We propose the Prosodic DAA by deriving the inference procedure for Prosodic HDP-HLM and show that Prosodic DAA can discover words directly from continuous human speech signals using statistical information and prosodic information in an unsupervised manner; 3) We show that prosodic cues contribute to word segmentation more in naturally distributed case words, i.e., they follow Zipf's law.Comment: 11 pages, Submitted to IEEE Transactions on Cognitive and Developmental System

Preparation of surfactant-free vinyl polymers by conventional emulsion polymerization using hydrolysable emulsifiers

Emulsion polymerizations of several vinyl monomers, styrene, methyl methacrylate, butyl methacrylate, butyl acrylate, and vinyl acetate, in water using alkali-hydrolysable cationic surfactants with a betaine ester group (1-alkoxycarbonylmethyl)trimethylammonium chlorides, as emulsifiers were carried out and properties of the resulting latices and the polymers recovered by hydrolysis and salting out were investigated. There were little influences of the surfactants and monomers used here on the polymerizations, forming stable and monodisperse latices with a mean diameter of ca. 70 nm and giving a high molecular weight of polymers at high yields. All polymers were precipitated and recovered by adding a small amount of sodium hydroxide into the latex solutions contained little amount of ionic species. Solvent-cast films of the polymers were found to have surfaces as hydrophobic as those for the corresponding pure polymers prepared by bulk polymerization.ArticlePOLYMER BULLETIN. 67(8):1455-1462 (2011)journal articl

Endothelial Progenitor Cells Promote Directional Three-Dimensional Endothelial Network Formation by Secreting Vascular Endothelial Growth Factor

Endothelial progenitor cell (EPC) transplantation induces the formation of new blood-vessel networks to supply nutrients and oxygen, and is feasible for the treatment of ischemia and cardiovascular diseases. However, the role of EPCs as a source of proangiogenic cytokines and consequent generators of an extracellular growth factor microenvironment in three-dimensional (3D) microvessel formation is not fully understood. We focused on the contribution of EPCs as a source of proangiogenic cytokines on 3D microvessel formation using an in vitro 3D network model. To create a 3D network model, EPCs isolated from rat bone marrow were sandwiched with double layers of collagen gel. Endothelial cells (ECs) were then cultured on top of the upper collagen gel layer. Quantitative analyses of EC network formation revealed that the length, number, and depth of the EC networks were significantly enhanced in a 3D model with ECs and EPCs compared to an EC monoculture. In addition, conditioned medium (CM) from the 3D model with ECs and EPCs promoted network formation compared to CM from an EC monoculture. We also confirmed that EPCs secreted vascular endothelial growth factor (VEGF). However, networks cultured with the CM were shallow and did not penetrate the collagen gel in great depth. Therefore, we conclude that EPCs contribute to 3D network formation at least through indirect incorporation by generating a local VEGF gradient. These results suggest that the location of EPCs is important for controlling directional 3D network formation in the field of tissue engineering

Mohawk promotes the maintenance and regeneration of the outer annulus fibrosus of intervertebral discs.

The main pathogenesis of intervertebral disc (IVD) herniation involves disruption of the annulus fibrosus (AF) caused by ageing or excessive mechanical stress and the resulting prolapse of the nucleus pulposus. Owing to the avascular nature of the IVD and lack of understanding the mechanisms that maintain the IVD, current therapies do not lead to tissue regeneration. Here we show that homeobox protein Mohawk (Mkx) is a key transcription factor that regulates AF development, maintenance and regeneration. Mkx is mainly expressed in the outer AF (OAF) of humans and mice. In Mkx(-/-) mice, the OAF displays a deficiency of multiple tendon/ligament-related genes, a smaller OAF collagen fibril diameter and a more rapid progression of IVD degeneration compared with the wild type. Mesenchymal stem cells overexpressing Mkx promote functional AF regeneration in a mouse AF defect model, with abundant collagen fibril formation. Our results indicate a therapeutic strategy for AF regeneration