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.