Customization of IBM Intu’s Voice by Connecting Text-to-Speech Services and a Voice Conversion Network

IBM has recently launched Project Intu, which extends the existing web-based cognitive service Watson with the Internet of Things to provide an intelligent personal assistant service. We propose a voice customization service that allows a user to directly customize the voice of Intu. The method for voice customization is based on IBM Watson’s text-to-speech service and voice conversion model. A user can train the voice conversion model by providing a minimum of approximately 100 speech samples in the preferred voice (target voice). The output voice of Intu (source voice) is then converted into the target voice. Furthermore, the user does not need to offer parallel data for the target voice since the transcriptions of the source speech and target speech are the same. We also suggest methods to maximize the efficiency of voice conversion and determine the proper amount of target speech based on several experiments. When we measured the elapsed time for each process, we observed that feature extraction accounts for 59.7% of voice conversion time, which implies that fixing inefficiencies in feature extraction should be prioritized. We used the mel-cepstral distortion between the target speech and reconstructed speech as an index for conversion accuracy and found that, when the number of target speech samples for training is less than 100, the general performance of the model degrades

Capillary-valve-based fabrication of ion-selective membrane junction for electrokinetic sample preconcentration in PDMS chip

In this paper, we report a novel method for fabricating ion-selective membranes in poly(dimethylsiloxane) (PDMS)/glass-based microfluidic preconcentrators. Based on the concept of capillary valves, this fabrication method involves filling a lithographically patterned junction between two microchannels with an ion-selective material such as Nafion resin; subsequent curing results in a high aspect-ratio membrane for use in electrokinetic sample preconcentration. To demonstrate the concentration performance of this high-aspect-ratio, ion-selective membrane, we integrated the preconcentrator with a surface-based immunoassay for R-Phycoerythrin (RPE). Using a 1× PBS buffer system, the preconcentrator-enhanced immunoassay showed an approximately 100× improvement in sensitivity within 30 min. This is the first time that an electrokinetic microfluidic preconcentrator based on ion concentration polarization (ICP) has been used in high ionic strength buffer solutions to enhance the sensitivity of a surface-based immunoassay.National Institutes of Health (U.S.) (CA119402)National Institutes of Health (U.S.) (EB005743

Nanofluidic Concentration Device for Biomolecules Utilizing Ion Concentration Polarization: Theory, Fabrication, and Applications

This article was published as part of the From microfluidic application to nanofluidic phenomena issueRecently, a new type of electrokinetic concentration devices has been developed in a microfluidic chip format, which allows efficient trapping and concentration of biomolecules by utilizing ion concentration polarization near nanofluidic structures. These devices have drawn much attention not only due to their potential application in biomolecule sensing, but also due to the rich scientific content related to ion concentration polarization, the underlying physical phenomenon for the operation of these electrokinetic concentration devices. This tutorial review provides an introduction to the scientific and engineering advances achieved, in-depth discussion about several interesting applications of these unique concentration devices, and their current limitations and challenges.National Science Foundation (U.S.) (CBET-0347348 & 0854026)National Institutes of Health (U.S.) (Grant EB005743)National Institutes of Health (U.S.) (Grant CA119402)National Institutes of Health (U.S.) (Grant P50-GM68762

Electrochemical activation and inhibition of neuromuscular systems through modulation of ion concentrations with ion-selective membranes

Conventional functional electrical stimulation aims to restore functional motor activity of patients with disabilities resulting from spinal cord injury or neurological disorders. However, intervention with functional electrical stimulation in neurological diseases lacks an effective implantable method that suppresses unwanted nerve signals. We have developed an electrochemical method to activate and inhibit a nerve by electrically modulating ion concentrations in situ along the nerve. Using ion-selective membranes to achieve different excitability states of the nerve, we observe either a reduction of the electrical threshold for stimulation by up to approximately 40%, or voluntary, reversible inhibition of nerve signal propagation. This low-threshold electrochemical stimulation method is applicable in current implantable neuroprosthetic devices, whereas the on-demand nerve-blocking mechanism could offer effective clinical intervention in disease states caused by uncontrolled nerve activation, such as epilepsy and chronic pain syndromes.Massachusetts Institute of Technology. Faculty Discretionary Research FundNational Institutes of Health (U.S.) (Award UL1 RR 025758)Harvard Catalyst (Grant

Enhancing Protease Activity Assay in Droplet-Based Microfluidics Using a Biomolecule Concentrator

We introduce an integrated microfluidic device consisting of a biomolecule concentrator and a microdroplet generator, which enhances the limited sensitivity of low-abundance enzyme assays by concentrating biomolecules before encapsulating them into droplet microreactors. We used this platform to detect ultralow levels of matrix metalloproteinases (MMPs) from diluted cellular supernatant and showed that it significantly (~10-fold) reduced the time required to complete the assay and the sample volume used.National Institutes of Health (U.S.) (Grant GM68762)National Institutes of Health (U.S.) (Grant U54-CA112967)National Institutes of Health (U.S.) (Grant R01-EB010246)National Institutes of Health (U.S.) (Grant R01-GM081336)National Science Foundation (U.S.) (Graduate Fellowship)United States. Defense Advanced Research Projects Agency (Cipher Program

Creating Sub-50 Nm Nanofluidic Junctions in PDMS Microfluidic Chip via Self-Assembly Process of Colloidal Particles

Polydimethylsiloxane (PDMS) is the prevailing building material to make microfluidic devices due to its ease of molding and bonding as well as its transparency. Due to the softness of the PDMS material, however, it is challenging to use PDMS for building nanochannels. The channels tend to collapse easily during plasma bonding. In this paper, we present an evaporation-driven self-assembly method of silica colloidal nanoparticles to create nanofluidic junctions with sub-50 nm pores between two microchannels. The pore size as well as the surface charge of the nanofluidic junction is tunable simply by changing the colloidal silica bead size and surface functionalization outside of the assembled microfluidic device in a vial before the self-assembly process. Using the self-assembly of nanoparticles with a bead size of 300 nm, 500 nm, and 900 nm, it was possible to fabricate a porous membrane with a pore size of ~45 nm, ~75 nm and ~135 nm, respectively. Under electrical potential, this nanoporous membrane initiated ion concentration polarization (ICP) acting as a cation-selective membrane to concentrate DNA by ~1,700 times within 15 min. This non-lithographic nanofabrication process opens up a new opportunity to build a tunable nanofluidic junction for the study of nanoscale transport processes of ions and molecules inside a PDMS microfluidic chip.National Institutes of Health (U.S.) (NIH R21 EB008177-01A2)New York University Abu Dhabi (NYUAD) (Research Enhancement Fund 2013

Rare Tokens Degenerate All Tokens: Improving Neural Text Generation via Adaptive Gradient Gating for Rare Token Embeddings

Recent studies have determined that the learned token embeddings of large-scale neural language models are degenerated to be anisotropic with a narrow-cone shape. This phenomenon, called the representation degeneration problem, facilitates an increase in the overall similarity between token embeddings that negatively affect the performance of the models. Although the existing methods that address the degeneration problem based on observations of the phenomenon triggered by the problem improves the performance of the text generation, the training dynamics of token embeddings behind the degeneration problem are still not explored. In this study, we analyze the training dynamics of the token embeddings focusing on rare token embedding. We demonstrate that the specific part of the gradient for rare token embeddings is the key cause of the degeneration problem for all tokens during training stage. Based on the analysis, we propose a novel method called, adaptive gradient gating (AGG). AGG addresses the degeneration problem by gating the specific part of the gradient for rare token embeddings. Experimental results from language modeling, word similarity, and machine translation tasks quantitatively and qualitatively verify the effectiveness of AGG.N