Publikation
Speech-overlapped Acoustic Event Detection for Automotive Applications
Christian Müller; Joan-Isaac Biel; Edward Kim; Daniel Rosario
In: Proceedings of the Interspeech 2008. Conference in the Annual Series of Interspeech Events (INTERSPEECH-2008), September 22-26, Brisbane, Australia, 2008.
Zusammenfassung
We present two approaches on acoustic event detection for
speech-enabled car applications: a generative GMM-UBM approach
and a discriminative GMM-SVM supervector approach.
The systems detect whether or not a certain acoustic event occurred
while the built-in microphone of the car was active to
record a spoken command, either before, while, or after the
driver was speaking. These events can be music playing, phone
ringing, a passenger different from the driver is talking, laughing,
or coughing. The task is formally defined as a detection
task along the lines of well established detection tasks such
as speaker recognition or language recognition. Similarly, the
evaluation procedure has been designed to resemble the respective
official evaluation series performed by NIST (i.e. it was
a blind - one-shot - evaluation on a separately provided dataset).
The performance of the system was calculated in terms of detection
miss and false alarm probabilities (CMiss = CFA = 1,
and PTarget = 0.5). The performance of the superior GMMSVM
system was 0.0345 for known test speakers and 0.1955
for novel test speakers. Frequency-filtered band energy coefficients
(FFBE) outperformed MFCCS on that task. The results
are promising and suggest further experiments on more data.