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Singapore’s Civil Defence Force turns to artificial intelligence to help emergency call dispatchers

Singapore’s Civil Defence Force turns to artificial intelligence to help emergency call dispatchers

Every year, Singapore's emergency dispatch
phone operators field approximately 200,000 calls. In high-stress situations that
phone operators often find themselves in, every minute is vital.

Thus, in an effort to ease their workload,
the Singapore Civil
Defence Force
(SCDF) and four other Government agencies will reportedly
be turning to artificial intelligence (AI) to help enhance efficiency. This
will be done using a speech recognition system that can transcribe and log each
call received in real time – even if it is in Singlish (Singaporean English).

Currently, the system is programmed to recognise English and Mandarin with some
Hokkien and Malay, though it could be customised
to incorporate other native dialects.

AI Singapore (AISG), a
programme under the National Research Foundation, is investing S$1.70 million
to set up the AI Speech Lab, led by the two professors who created the system. The
system was developed using artificial intelligence, such as deep learning
technology, which works off algorithms that mimic the human brain's neural
pathways to help computers perform new tasks and analyse data.

SCDF's Director of Operations,
Assistant Commissioner, Daniel Seet, said, "If successful, it will improve
how SCDF's emergency medical resources are dispatched and enhance the overall
health outcomes of those in need."

He added that the new
system will help reduce the time it takes the SCDF's 995 operations centre dispatchers to log incoming information.

Dispatchers ask the
caller questions to determine the nature and severity of the case, to make sure
the appropriate emergency medical resources are sent.

The AI Speech Lab is headed
by Professor Li Haizhou, an expert in speech, text and natural language
processing from the National University of Singapore, and Associate Professor
Chng Eng Siong from the Nanyang Technological University. Together, the two have
been working on the speech recognition system for nearly ten years!

Prof Li stated that a
code-switching, or mixed-lingual, state-of-the-art system such as this, is
currently commercially unavailable.
"This technology performs better than commercial engines as it can
accurately recognise conversations
comprising words from different languages. It solves a unique Singapore problem,"
he said.

To develop the system,
researchers collected over 1,000 hours of speaking samples from Singapore and
Penang – a state that mixes languages in speech similar to Singapore – as well
as recordings of Singaporeans from radio stations, YouTube and SoundCloud.

These recordings are
manually transcribed into text. The system then "learns" the
association between the text and the collected speech samples.

The system has "learnt" around 40,000 English and Mandarin
words each, and has an accuracy rate of about 90 per cent.

Unique words the
system can recognise include "jiak ba bueh"
and "hoh boh" – "have you
eaten" and "how are you" in Hokkien – and local dishes such as
char kway teow and nasi lemak.

The lab is staffed by
five AI engineers and located at the innovation 4.0 building on NUS's Kent
Ridge campus.

Professor Ho Teck Hua,
executive chairman of AISG, said the system could also benefit companies, as it
can be customised according to their
business needs.

Mr Tan Kok Yam, deputy secretary of the Smart
Nation and Digital Government agency, said: "The Government is keen to
harness artificial intelligence to serve our citizens better. GovTech is
collaborating with AISG to develop solutions that can improve planning and
service delivery."

Research director at
research and advisory company Gartner, Mr
Manjunath Bhat, said: "Multi-lingual speech transcription will make it
easy for senior citizens and people speaking all dialects to participate in
digital initiatives.

"Even as
communication systems switch from analogue
to digital, human language itself remains analog. The new solution enables
computers to speak in the language of the common person as opposed to humans
learning to adapt to digital interfaces."