Automatic Speech Analysis Framework for ATC Communication in HAAWAII

Over the past years, several SESAR funded ex- ploratory projects focused on
bringing speech and language technologies to the Air Traffic Management (ATM)
domain and demonstrating their added value through successful applications.
Recently ended HAAWAII project developed a generic archi- tecture and framework,
which was validated through several tasks such as callsign highlighting,
pre-filling radar labels, and readback error detection. The primary goal was to
support pilot and air traffic controller communication by deploying Automatic
Speech Recognition (ASR) engines. Contextual information (if available) extracted
from surveillance data, flight plan data, or previous communication can be
exploited via entity boosting to further improve the recognition performance.
HAAWAII proposed various design attributes to integrate the ASR engine into the
ATM framework, often depending on concrete technical specifics of target air
navigation service providers (ANSPs). This paper gives a brief overview and
provides an objective assessment of speech processing components developed and
integrated into the HAAWAII framework. Specifically, the following tasks are
evaluated w.r.t. application domain: (i) speech activity detection, (ii) speaker
segmentation and speaker role classification, as well as (iii) ASR. To our best
knowledge, HAAWAII framework offers the best performing speech technologies for
ATM, reaching high recognition accuracy (i.e., error-correction done by
exploiting additional contextual data), robustness (i.e., models developed using
large training corpora) and support for rapid domain transfer (i.e., to new ATM
sector with minimum investment). Two scenarios provided by ANSPs were used for
testing, achieving callsign detection accuracy of about 96% and 95% for NATS and
ISAVIA, respectively.