aiOla's model automates the creation of
customized processes and workflows for conducting reports and
inspections across industries such as manufacturing, supply chain
and logistics, pharma, and more
TEL
AVIV, Israel, April 18,
2024 /PRNewswire/ -- aiOla, an AI-powered technology
that automates business workflows by capturing spoken data, has
announced a major milestone in speech recognition. aiOla's
solution, powered by a novel keyword spotting model, has advanced
to match human proficiency in understanding industry-specific
jargon. The patented AdaKWS model achieved 95% accuracy in keyword
spotting, surpassing OpenAI's industry-leading Whisper model which
reached 88% accuracy.
Keyword spotting is an essential aspect of speech recognition
that tackles the problem of identifying jargon by detecting
predefined words and phrases. "Think about a courier delivery where
your package arrives damaged. The courier needs to file a report
using specific codes and acronyms that describe the situation —
those codes and acronyms are keywords. Industry jargon is
everywhere and in many fields, it dominates communication,
comprising up to half of workers' speech," said aiOla's CEO and
co-founder, Amir Haramaty. "The
ability to spot keywords enables automation of everyday processes
across a wide range of industries, from filing a parcel damage
report to completing a safety inspection in a food manufacturing
plant, transforming speech into actions."
aiOla's process automation applications can accurately
understand speech, jargon and acronyms across over 100 languages,
regardless of accents and background noises. aiOla achieves this by
combining its state-of-the-art keyword spotting model with a speech
recognition model. The onboarding process takes mere hours: clients
provide examples of their checklists or forms, and aiOla
automatically generates custom language models for the use case.
Workers are then able to complete their operations verbally using
the aiOla app while keeping their eyes and hands on the equipment.
aiOla's exceptional ability to spot rare industry terms with high
accuracy allows the platform to easily distinguish between speech
related to work processes and everyday conversation.
The app leverages a proprietary model that was developed by
aiOla's team of scientists to recognize a predefined list of
keywords within speech. This enables aiOla's solution to be
instantly adapted to the jargon of any industry without needing to
retrain its AI model. On a benchmark of keyword and jargon
detection that includes 16 languages, Whisper's largest model
yields 88% accuracy compared to aiOla's model achieving 95%
accuracy. Additionally, in a recent benchmark which is composed of
hard-to-detect keywords taken from English language audiobooks, the
CED model from a team of Apple researchers yields 92.7% whereas
aiOla's AdaKWS reaches 95.1% accuracy.
"Keyword spotting poses significant challenges due to the
scarcity of training data, especially across diverse languages and
dialects. It typically requires industry-specific fine-tuning to
enable models to recognize jargon not commonly found in everyday
speech," said aiOla's Chief Scientist, Professor Joseph Keshet. "Our model consistently surpassed
the OpenAI Whisper baselines by a significant margin, achieving a
substantial improvement compared to the top-performing baseline.
Furthermore, our model is far more efficient, using 15x fewer
parameters."
To learn more about aiOla's technology visit:
https://aiola.com
Explore aiOla's keyword spotting research:
https://arxiv.org/pdf/2309.08561.pdf
About aiOla:
aiOla's patented technology comprehends over 100 languages, and
discerns jargon, abbreviations and acronyms, demonstrating a low
error rate even in noisy environments. aiOla's technology converts
manual processes in critical industries into data-driven,
paperless, AI-powered workflows through cutting-edge speech
recognition.
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SOURCE aiOla