👂 🎴 🕸️
Human
-
Machine
Peer
Learning
(
HMPL
)
provides
a
paradigm
for
construction
of
such
human
-
machine
learning
curricula
from
which
both
humans
as
well
as
machines
benefit
.<
p
class
=
„
fragment
“
>
In
our
previous
HMPL
Curriculum
1
(
HMPL
-
C1
)
study
which
focused
on
extending
foreign
language
vocabulary
for
human
learners
and
increase
of
speech
-
recognition
accuracy
of
artificial
learners
''
we
have
„
observed
increase
in
amount
of
matches
between
expected
and
predicted
labels
which
was
caused
both
by
increase
of
human
learner
’
s
vocabulary
''
as
well
as
by
increase
of
recognition
accuracy
of
machine
’
s
speech
-
to
-
text
model
“
(
Hromada
&
Kim
''
„
<
em
>
Proof
-
of
-
concept
of
feasibility
of
human
–
machine
peer
learning
for
German
noun
vocabulary
learning
em
>
“
''
Frontiers
in
Education
''
2023
)
p
>
<
p
class
=
„
fragment
“
>
in
majority
of
reading
exercises
which
are
included
in
the
Primer
we
already
know
the
text
in
advance
p
><
p
class
=
„
fragment
“
>
we
already
know
what
utterances
could
be
considered
as
correct
lectures
and
which
not
p
><
p
class
=
„
fragment
“
>
to
every
specific
exercise
-
like
vowel
or
syllable
recognition
–
Primer
associates
a
specific
language
model
(
scorer
)
which
constraints
the
connectionist
temporal
classification
(
CTC
)
beam
search
to
restricted
amount
of
exercise
-
relevant
answers
p
><
p
class
=
„
fragment
“
>
significantly
constraining
the
search
space
of
plausible
solutions
p
><
p
class
=
„
fragment
“
>
INNOVATION
1
:
implementation
of
exercise
-
specific
scorers
transforms
a
generic
ASR
problem
(
difficult
)
into
multi
-
class
classification
problem
(
easier
)
p
>
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