Volume: 16,
Issue: 1, September 2013Using SNR Knowledge to Improve
Noise-Robust Speech Recognition Normalization
Authors: Nathan Sherwood, Julian
Cardenas Barreras, Eduardo Castillo Guerra, Julian Meng
Abstract: We explore the benefits of using signal to noise ratio (SNR) knowledge
to increase performance in noise-robust speech recognition. With this knowledge
we look at the advantages behind using models built around specific SNR levels,
as well as look at how we can incorporate that knowledge into feature
extraction techniques. We have chosen to enhance temporal structure
normalization (TSN), which normalizes the trend of noise in the features power
spectral density (PSD). The performance of this technique was evaluated using
the SPHINX-III speech recognition system with a modified version of the TIDIGITS
speech corpus. Noise was taken from the NOIZEUS database and mixed in at
varying SNR levels ranging from -5 dB to 20 dB (in increments of 5). Results
show that SNR-dependent models are more effective and incorporating knowledge
of noise into speech processing techniques can increase the performance of the
speech recognition system with very little computational cost.
(pp. 812-815)
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| Fuzzy-Based Recognition
Model for Driving Styles
Authors: Maen Saleh, Ahmad
Aljaafreh, Nashat Albdour
Abstract:
Driving styles is not only important for
safety but also for fuel economy and emissions. Driving style is related to the
driver behaviour as well as the driving condition. Abnormal driving behaviours
can be detected by either monitoring of drivers or monitoring of vehicle-human
interactions. In this paper, we propose a fuzzy system that classifies the
driving styles (in-terms of vehicle-human interactions) into four classes; Below
Normal (BN), Normal (N), Aggressive (A) and Very Aggressive (VA). The proposed
classifier was built based on three main attributes: longitudinal acceleration,
car velocity and the distance between the preceding and host car. Compared with
data obtained from an expert judgment for a 25 driving styles, our proposed
system shows an efficiency and reliability with a rate of 72% with the
ignorance of fuzzy rules tuning process.
(pp. 816-819)
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| Driving Styles Recognition Using Decomposed
Fuzzy Logic System
Authors: Munaf Najim Al-Din, Ahmad
Aljaafreh, Nashat Albdour, Maen Saleh
Abstract:
Driver behaviour recognition or driving
style classification systems have become very important for reducing vehicle
accidents and providing safety. Human driver behavior recognition systems are
used to estimate human behaviour by observing driver interaction with the
vehicle and environment. These systems goal is to infer the driver's behaviour
by mapping some parameters that represent the driver interaction with control
elements available in the vehicle, such as speed, accelerator, following
distance, and etc. The driving style recognition approach developed in this
paper is based on the theory of fuzzy logic, which provides a methodology for reasoning
about the application area that approximates human reasoning. One key aspect of
the method developed in this paper is the great reduction of the rules of the
rule-base inference engine by using a decomposed fuzzy system. The method is based
on introducing weighting factors for the sensor inputs, thus inferring the
reflexive conclusions from each input to the system rather than putting all the
possible states of all the inputs to infer a single conclusion. The
effectiveness and reliability of the proposed method is evaluated and compared
with data obtained from an expert judgment for a 25 driving styles.
(pp. 820-824)
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