9,261 research outputs found
Personalized Acoustic Modeling by Weakly Supervised Multi-Task Deep Learning using Acoustic Tokens Discovered from Unlabeled Data
It is well known that recognizers personalized to each user are much more
effective than user-independent recognizers. With the popularity of smartphones
today, although it is not difficult to collect a large set of audio data for
each user, it is difficult to transcribe it. However, it is now possible to
automatically discover acoustic tokens from unlabeled personal data in an
unsupervised way. We therefore propose a multi-task deep learning framework
called a phoneme-token deep neural network (PTDNN), jointly trained from
unsupervised acoustic tokens discovered from unlabeled data and very limited
transcribed data for personalized acoustic modeling. We term this scenario
"weakly supervised". The underlying intuition is that the high degree of
similarity between the HMM states of acoustic token models and phoneme models
may help them learn from each other in this multi-task learning framework.
Initial experiments performed over a personalized audio data set recorded from
Facebook posts demonstrated that very good improvements can be achieved in both
frame accuracy and word accuracy over popularly-considered baselines such as
fDLR, speaker code and lightly supervised adaptation. This approach complements
existing speaker adaptation approaches and can be used jointly with such
techniques to yield improved results.Comment: 5 pages, 5 figures, published in IEEE ICASSP 201
Unsupervised Spoken Term Detection with Spoken Queries by Multi-level Acoustic Patterns with Varying Model Granularity
This paper presents a new approach for unsupervised Spoken Term Detection
with spoken queries using multiple sets of acoustic patterns automatically
discovered from the target corpus. The different pattern HMM
configurations(number of states per model, number of distinct models, number of
Gaussians per state)form a three-dimensional model granularity space. Different
sets of acoustic patterns automatically discovered on different points properly
distributed over this three-dimensional space are complementary to one another,
thus can jointly capture the characteristics of the spoken terms. By
representing the spoken content and spoken query as sequences of acoustic
patterns, a series of approaches for matching the pattern index sequences while
considering the signal variations are developed. In this way, not only the
on-line computation load can be reduced, but the signal distributions caused by
different speakers and acoustic conditions can be reasonably taken care of. The
results indicate that this approach significantly outperformed the unsupervised
feature-based DTW baseline by 16.16\% in mean average precision on the TIMIT
corpus.Comment: Accepted by ICASSP 201
A microfluidic oligonucleotide synthesizer
De novo gene and genome synthesis enables the design of any sequence without the requirement of a pre-existing template as in traditional genetic engineering methods. The ability to mass produce synthetic genes holds great potential for biological research, but widespread availability of de novo DNA constructs is currently hampered by their high cost. In this work, we describe a microfluidic platform for parallel solid phase synthesis of oligonucleotides that can greatly reduce the cost of gene synthesis by reducing reagent consumption (by 100-fold) while maintaining a 100 pmol synthesis scale so there is no need for amplification before assembly. Sixteen oligonucleotides were synthesized in parallel on this platform and then successfully used in a ligation-mediated assembly method to generate DNA constructs 200 bp in length
Effect of an electric field on the growth of aluminum film
[[abstract]]An electric field is applied to the substrate during the growth of aluminum films, with the results that the reflectance is increased, scattering is reduced, and the surface is smoothed.[[fileno]]2030150010027[[department]]電機工程å¸
6.4 GHz Acoustic Sensor for In-situ Monitoring of AFM Tip Wear
This paper demonstrates an acoustic sensor that can resolve atomic force microscopy (AFM) tip blunting with a frequency sensitivity of 0.007%. The AFM tip is fabricated on a thin film piezoelectric aluminum nitride (AlN) membrane that is excited as a film bulk acoustic resonator (FBAR). We demonstrate that cutting 0.98 μm off of the tip apex results in a resonance frequency change of 0.4MHz at 6.387GHz. This work demonstrates the potential for in-situ monitoring of AFM tip wear
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