  Furui first demonstrated that the identity of both consonant and vowel can be
perceived from the C-V transition; later, Stevens proposed that acoustic
landmarks are the primary cues for speech perception, and that steady-state
regions are secondary or supplemental. Acoustic landmarks are perceptually
salient, even in a language one doesn't speak, and it has been demonstrated
that non-speakers of the language can identify features such as the primary
articulator of the landmark. These factors suggest a strategy for developing
language-independent automatic speech recognition: landmarks can potentially be
learned once from a suitably labeled corpus and rapidly applied to many other
languages. This paper proposes enhancing the cross-lingual portability of a
neural network by using landmarks as the secondary task in multi-task learning
(MTL). The network is trained in a well-resourced source language with both
phone and landmark labels (English), then adapted to an under-resourced target
language with only word labels (Iban). Landmark-tasked MTL reduces
source-language phone error rate by 2.9% relative, and reduces target-language
word error rate by 1.9%-5.9% depending on the amount of target-language
training data. These results suggest that landmark-tasked MTL causes the DNN to
learn hidden-node features that are useful for cross-lingual adaptation.
