Uncertainty Decoding with Adaptive Sampling for Noise Robust DNN-Based Acoustic Modeling

2017 ◽  
Author(s):  
Dung T. Tran ◽  
Marc Delcroix ◽  
Atsunori Ogawa ◽  
Tomohiro Nakatani
Keyword(s):  
Adaptive Sampling ◽  
Acoustic Modeling ◽  
Noise Robust

Performance Analysis of various Front-end and Back End Amalgamations for Noise-robust DNN-based ASR

2020 ◽  
Vol 13 ◽  
Author(s):  
Mohit Dua ◽  
Pawandeep Singh Sethi ◽  
Vinam Agrawal ◽  
Raghav Chawla
Keyword(s):  
Feature Extraction ◽  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Gaussian Mixture ◽  
Performance Comparison ◽  
Acoustic Modeling ◽  
Extraction Techniques ◽  
Front End ◽  
Noise Robust ◽  
Asr System

Introduction: An Automatic Speech Recognition (ASR) system enables to recognize the speech utterances and thus can be used to convert speech into text for various purposes. These systems are deployed in different environments such as clean or noisy and are used by all ages or types of people. These also present some of the major difficulties faced in the development of an ASR system. Thus, an ASR system need to be efficient, while also being accurate and robust. Our main goal is to minimize the error rate during training as well as testing phases, while implementing an ASR system. Performance of ASR depends upon different combinations of feature extraction techniques and back-end techniques. In this paper, using a continuous speech recognition system, the performance comparison of different combinations of feature extraction techniques and various types of back-end techniques has been presented Methods: Hidden Markov Models (HMMs), Subspace Gaussian Mixture Models (SGMMs) and Deep Neural Networks (DNNs) with DNN-HMM architecture, namely Karel's, Dan's and Hybrid DNN-SGMM architecture are used at the back-end of the implemented system. Mel frequency Cepstral Coefficient (MFCC), Perceptual Linear Prediction (PLP), and Gammatone Frequency Cepstral coefficients (GFCC) are used as feature extraction techniques at the front-end of the proposed system. Kaldi toolkit has been used for the implementation of the proposed work. The system is trained on the Texas Instruments-Massachusetts Institute of Technology (TIMIT) speech corpus for English language Results: The experimental results show that MFCC outperforms GFCC and PLP in noiseless conditions, while PLP tends to outperform MFCC and GFCC in noisy conditions. Furthermore, the hybrid of Dan's DNN implementation along with SGMM performs the best for the back-end acoustic modeling. The proposed architecture with PLP feature extraction technique in the front end and hybrid of Dan's DNN implementation along with SGMM at the back end outperforms the other combinations in a noisy environment. Conclusion: Automatic Speech recognition has numerous applications in our lives like Home automation, Personal assistant, Robotics etc. It is highly desirable to build an ASR system with good performance. The performance Automatic Speech Recognition is affected by various factors which include vocabulary size, whether system is speaker dependent or independent, whether speech is isolated, discontinuous or continuous, adverse conditions like noise. The paper presented an ensemble architecture that uses PLP for feature extraction at the front end and a hybrid of SGMM + Dan's DNN in the backend to build a noise robust ASR system Discussion: The presented work in this paper discusses the performance comparison of continuous ASR systems developed using different combinations of front-end feature extraction (MFCC, PLP, and GFCC) and back-end acoustic modeling (mono-phone, tri-phone, SGMM, DNN and hybrid DNN-SGMM) techniques. Each type of front-end technique is tested in combination with each type of back-end technique. Finally, it compares the results of the combinations thus formed, to find out the best performing combination in noisy and clean conditions

Integrated Ambient Light Sensor with an LTPS Noise-Robust Circuit and a-Si Photodiodes for AMLCDs

2010 ◽  
Vol E93-C (11) ◽  
pp. 1583-1589
Author(s):  
Fumirou MATSUKI ◽  
Kazuyuki HASHIMOTO ◽  
Keiichi SANO ◽  
Fu-Yuan HSUEH ◽  
Ramesh KAKKAD ◽  
...  
Keyword(s):  
Ambient Light ◽  
Light Sensor ◽  
Noise Robust

Nanosecond Photodynamics Simulations of a Cis-Trans Isomerization Are Enabled by Machine Learning

2020 ◽  
Author(s):  
Jingbai Li ◽  
Patrick Reiser ◽  
André Eberhard ◽  
Pascal Friederich ◽  
Steven Lopez
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Excited State ◽  
Adaptive Sampling ◽  
Computational Cost ◽  
Ground Truth ◽  
Absolute Error ◽  
Photochemical Reactions ◽  
Computational Techniques ◽  
Full Potential

<p>Photochemical reactions are being increasingly used to construct complex molecular architectures with mild and straightforward reaction conditions. Computational techniques are increasingly important to understand the reactivities and chemoselectivities of photochemical isomerization reactions because they offer molecular bonding information along the excited-state(s) of photodynamics. These photodynamics simulations are resource-intensive and are typically limited to 1–10 picoseconds and 1,000 trajectories due to high computational cost. Most organic photochemical reactions have excited-state lifetimes exceeding 1 picosecond, which places them outside possible computational studies. Westermeyr <i>et al.</i> demonstrated that a machine learning approach could significantly lengthen photodynamics simulation times for a model system, methylenimmonium cation (CH<sub>2</sub>NH<sub>2</sub><sup>+</sup>).</p><p>We have developed a Python-based code, Python Rapid Artificial Intelligence <i>Ab Initio</i> Molecular Dynamics (PyRAI<sup>2</sup>MD), to accomplish the unprecedented 10 ns <i>cis-trans</i> photodynamics of <i>trans</i>-hexafluoro-2-butene (CF<sub>3</sub>–CH=CH–CF<sub>3</sub>) in 3.5 days. The same simulation would take approximately 58 years with ground-truth multiconfigurational dynamics. We proposed an innovative scheme combining Wigner sampling, geometrical interpolations, and short-time quantum chemical trajectories to effectively sample the initial data, facilitating the adaptive sampling to generate an informative and data-efficient training set with 6,232 data points. Our neural networks achieved chemical accuracy (mean absolute error of 0.032 eV). Our 4,814 trajectories reproduced the S<sub>1</sub> half-life (60.5 fs), the photochemical product ratio (<i>trans</i>: <i>cis</i> = 2.3: 1), and autonomously discovered a pathway towards a carbene. The neural networks have also shown the capability of generalizing the full potential energy surface with chemically incomplete data (<i>trans</i> → <i>cis</i> but not <i>cis</i> → <i>trans</i> pathways) that may offer future automated photochemical reaction discoveries.</p>

Improved acoustic modeling for continuous speech recognition

1990 ◽  
Author(s):  
C.-H. Lee ◽  
E. Giachin ◽  
L. R. Rabiner ◽  
R. Pieraccini ◽  
A. E. Rosenberg
Keyword(s):  
Speech Recognition ◽  
Acoustic Modeling ◽  
Continuous Speech ◽  
Continuous Speech Recognition
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