494 research outputs found
TripleNet: A Low Computing Power Platform of Low-Parameter Network
With the excellent performance of deep learning technology in the field of
computer vision, convolutional neural network (CNN) architecture has become the
main backbone of computer vision task technology. With the widespread use of
mobile devices, neural network models based on platforms with low computing
power are gradually being paid attention. This paper proposes a lightweight
convolutional neural network model, TripleNet, an improved convolutional neural
network based on HarDNet and ThreshNet, inheriting the advantages of small
memory usage and low power consumption of the mentioned two models. TripleNet
uses three different convolutional layers combined into a new model
architecture, which has less number of parameters than that of HarDNet and
ThreshNet. CIFAR-10 and SVHN datasets were used for image classification by
employing HarDNet, ThreshNet, and our proposed TripleNet for verification.
Experimental results show that, compared with HarDNet, TripleNet's parameters
are reduced by 66% and its accuracy rate is increased by 18%; compared with
ThreshNet, TripleNet's parameters are reduced by 37% and its accuracy rate is
increased by 5%.Comment: 4 pages, 2 figure
Probabilistic Speech-Driven 3D Facial Motion Synthesis: New Benchmarks, Methods, and Applications
We consider the task of animating 3D facial geometry from speech signal.
Existing works are primarily deterministic, focusing on learning a one-to-one
mapping from speech signal to 3D face meshes on small datasets with limited
speakers. While these models can achieve high-quality lip articulation for
speakers in the training set, they are unable to capture the full and diverse
distribution of 3D facial motions that accompany speech in the real world.
Importantly, the relationship between speech and facial motion is one-to-many,
containing both inter-speaker and intra-speaker variations and necessitating a
probabilistic approach. In this paper, we identify and address key challenges
that have so far limited the development of probabilistic models: lack of
datasets and metrics that are suitable for training and evaluating them, as
well as the difficulty of designing a model that generates diverse results
while remaining faithful to a strong conditioning signal as speech. We first
propose large-scale benchmark datasets and metrics suitable for probabilistic
modeling. Then, we demonstrate a probabilistic model that achieves both
diversity and fidelity to speech, outperforming other methods across the
proposed benchmarks. Finally, we showcase useful applications of probabilistic
models trained on these large-scale datasets: we can generate diverse
speech-driven 3D facial motion that matches unseen speaker styles extracted
from reference clips; and our synthetic meshes can be used to improve the
performance of downstream audio-visual models
Prediction of protein secondary structures with a novel kernel density estimation based classifier
A social networking approach for mobile innovation in emerging countries
Thesis (S.M. in Engineering and Management)--Massachusetts Institute of Technology, Engineering Systems Division, System Design and Management Program, February 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 121-122).Addressing the global challenges and the next billion mobile subscribers, the MIT NextLab course engages students, industry partners, entrepreneurs and the next billion mobile subscribers to develop innovative mobile services that improve the quality of life in the emerging countries. In three years, NextLab teams developed and deployed 29 projects in 14 counties, and five teams founded their own ventures after perceiving the strong demand from the vast mobile users in the developing world. However, the size and the amount of NextLab projects are limited by the schedule and the location of an academic course. The focus of this thesis is to research and develop a social networking platform that replicates the success of the NextLab course to reach out to more participants around the world. In this document, I utilized the social analysis framework to identify social processes among stakeholders in a general NextLab project, specify the possible social failures and research the possible solutions. Besides, I also reviewed the NextLab projects in 2008 and 2009 and developed the NextLab Project Development Process (NLPDP) that highlights the 12 critical stages of a NextLab project. Finally, I proposed the NextLab 2.0 Community that is integrates with the social networking solutions and the NextLab Project Development Process. The case study of the mobile logistics (m-Logistics) project is used to demonstrate how the proposed solution facilitates the collaboration and communication for a large and cross-country mobile innovation project. A number of recommendations were also discussed for further research.by Jen-Hao Yang.S.M.in Engineering and Managemen
Text is All You Need: Personalizing ASR Models using Controllable Speech Synthesis
Adapting generic speech recognition models to specific individuals is a
challenging problem due to the scarcity of personalized data. Recent works have
proposed boosting the amount of training data using personalized text-to-speech
synthesis. Here, we ask two fundamental questions about this strategy: when is
synthetic data effective for personalization, and why is it effective in those
cases? To address the first question, we adapt a state-of-the-art automatic
speech recognition (ASR) model to target speakers from four benchmark datasets
representative of different speaker types. We show that ASR personalization
with synthetic data is effective in all cases, but particularly when (i) the
target speaker is underrepresented in the global data, and (ii) the capacity of
the global model is limited. To address the second question of why personalized
synthetic data is effective, we use controllable speech synthesis to generate
speech with varied styles and content. Surprisingly, we find that the text
content of the synthetic data, rather than style, is important for speaker
adaptation. These results lead us to propose a data selection strategy for ASR
personalization based on speech content.Comment: ICASSP 202
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