2 research outputs found
Speech assessment and characterization for law enforcement applications
Speech signals acquired, transmitted or stored in non-ideal conditions are often degraded by
one or more effects including, for example, additive noise. These degradations alter the signal
properties in a manner that deteriorates the intelligibility or quality of the speech signal. In
the law enforcement context such degradations are commonplace due to the limitations in
the audio collection methodology, which is often required to be covert. In severe degradation
conditions, the acquired signal may become unintelligible, losing its value in an investigation
and in less severe conditions, a loss in signal quality may be encountered, which can lead to
higher transcription time and cost.
This thesis proposes a non-intrusive speech assessment framework from which algorithms for
speech quality and intelligibility assessment are derived, to guide the collection and transcription
of law enforcement audio. These methods are trained on a large database labelled using
intrusive techniques (whose performance is verified with subjective scores) and shown to perform
favorably when compared with existing non-intrusive techniques. Additionally, a non-intrusive
CODEC identification and verification algorithm is developed which can identify a CODEC with
an accuracy of 96.8 % and detect the presence of a CODEC with an accuracy higher than 97 %
in the presence of additive noise.
Finally, the speech description taxonomy framework is developed, with the aim of characterizing
various aspects of a degraded speech signal, including the mechanism that results in a signal
with particular characteristics, the vocabulary that can be used to describe those degradations
and the measurable signal properties that can characterize the degradations. The taxonomy is
implemented as a relational database that facilitates the modeling of the relationships between
various attributes of a signal and promises to be a useful tool for training and guiding audio
analysts