Title page for ETD etd-04122011-133223
|Type of Document
|Author's Email Address
||Fuzzy Analysis of Speech Metrics to Estimate Crew Alertness
|Southward, Steve C.
|Cooper, Robin K. Panneton
|Johnson, Martin E.
- Fuzzy Logic
- Operator Alertness
- Time Series
- Alertness Estimation
|Date of Defense
A novel approach for estimating alertness levels from speech and tagging them with a reliability
component has been developed. The Fatigue Quotient and Believability are both derived from
the time series analysis of the speech signal in the communication between the operator and
Operator attention is the most important human factor element for safe transportation operations.
In addition to substance abuse, illness and intoxication fatigue is a major contributing factor to
the decrease of attention. The goal of this study was to develop a means to detect and estimate
fatigue levels of railroad operating personnel during on-duty hours. This goal continues to gain
importance with new efforts from the government to expand rail transportation operations as a
tool for high speed mass transportation in urban areas. Previous research has shown that sleeping
disorders, reduced hours of rest and disrupted circadian rhythms lead to significantly increased
fatigue levels which manifest themselves in alterations of speech patterns as compared to alert
states of mind. In this study vocal indicators of fatigue are extracted from the speech signal and
Fuzzy Logic is used to generate an estimate of the cognitive state of the train conductor. The
output is tagged with a believability metric based on its behavior with respect to previous outputs
and a fully alert state. Communication between the conductor and dispatch over radio provides
an unobtrusive way of accessing the speech signal through existing speech infrastructure. The
speech signal is discretized and processed through a digital signal processing algorithm, which
extracts speech metrics from the signal that were determined to be indicative of fatigue levels.
Speech metrics include, but are not limited to, speech duration, silence duration, word production
rate, phrase gap duration, number of words per phrase and speech intensity. A fuzzy logic
minimum inference engine maps the inputs to an output through an empirically determined rule
base. The rule base and the associated membership functions were derived from batch mode and
real time testing and the subsequent tuning of parameters to refine the detection of changes in
patterns. To increase the validity and transparency of the output time series analysis is used to
create the believability metric. A moving average filter eliminates the short term fluctuations and
determines the long term trend of the output. A moving standard deviation estimation quantifies
instantaneous fluctuations and provides a measure of the difference to a nominal alertness state.
A real time version of the algorithm was developed and prototyped on a generic, low cost and
scalable hardware platform. Rapid Prototyping was realized through the Matlab/Simulink xPC
Target toolbox which allowed for instant real time code generation, testing and modification.
This testing environment together with batch mode testing was used to extensively test and fine
tune parameters to improve the performance of the algorithm. A testing procedure was developed
and standardized to collect data and tune the parameters of the algorithm. As a high level goal it
was proven that the concept of digital signal processing and Fuzzy Logic can be utilized to detect
changes in speech and estimate alertness levels from it. Furthermore, this study has proven that
the framework to run such an analysis continuously as a monitoring function in locomotive
cabins is feasible and can be realized with relatively inexpensive hardware. The development,
implementation and testing process conducted for this project is explained and results are
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