Adapting the acoustic model of a speech recognizer for varied proficiency non-native spontaneous speech using read speech with language-specific pronunciation difficulty

  • Zechner K
  • Higgins D
  • Lawless R
 et al. 
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Abstract

This paper presents a novel approach to acoustic model adaptation of a
recognizer for non-native spontaneous speech in the context of
recognizing candidates' responses in a test of spoken English. Instead
of collecting and then transcribing spontaneous speech data, a read
speech corpus is created where non-native speakers of English read
English sentences of different degrees of pronunciation difficulty with
respect to their native language. The motivation for this approach is
(1) to save time and cost associated with transcribing spontaneous
speech, and (2) to allow for a targeted training of the recognizer,
focusing particularly on those phoneme environments which are difficult
to pronounce correctly by non-native speakers and hence have a higher
likelihood of being misrecognized. As a criterion for selecting the
sentences to be read, we develop a novel score, the ``phonetic challenge
score{''}, consisting of a measure for native language-specific
difficulties described in the second-language acquisition literature and
also of a statistical measure based on the cross-entropy between phoneme
sequences of the native language and English.
We collected about 23,000 read sentences from 200 speakers in four
language groups: Chinese, Japanese, Korean, and Spanish. We used this
data for acoustic model adaptation of a spontaneous speech recognizer
and compared recognition performance between the unadapted baseline and
the system after adaptation on a held-out set from the English test
responses data set.
The results show that using this targeted read speech material for
acoustic model adaptation does reduce the word error rate significantly
for two of four language groups of the spontaneous speech test set,
while changes of the two other language groups are not significant.

Author-supplied keywords

  • Acoustic model adaptation
  • Cross-lingual phonetic difficulty
  • Non-native spontaneous speech

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Authors

  • Klaus Zechner

  • Derrick Higgins

  • René Lawless

  • Yoko Futagi

  • Sarah Ohls

  • George Ivanov

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