We propose to use the personalized speech synthesis and the neural
\nlanguage generator to synthesize content relevant personalized
\nspeech for rapid speaker adaptation. It has two distinct aspects:
\nFirst, it relieves the general data sparsity issue in rapid adaptation via
\nmaking use of additional synthesized personalized speech; Second,
\nit circumvents the obstacle of the explicit labeling error in unsupervised
\nadaptation by converting it to pseudo-supervised adaptation.
\nIn this setup, the labeling error is implicitly rendered as less damaging
\nspeech distortion in the personalized synthesized speech. This
\nresults in significant performance breakthrough in the rapid unsupervised
\nspeaker adaptation. We apply the proposed methodology to a
\nspeaker adaptation task in a state-of-art speech transcription system.
\nWith 1 minute (min) adaptation data, our proposed approach yields
\n9.19 % or 5.98 % relative word error rate (WER) reduction for the
\nsupervised and the unsupervised adaptation, comparing to the negligible
\ngain when adapting only with 1 min original speech. With 10
\nmin adaptation data, it yields 12.53 % or 7.89 % relative WER reduction,
\ndoubling the gain of the baseline adaptation. The proposed
\napproach is particularly suitable for unsupervised adaptation.<\/p>\n","protected":false},"excerpt":{"rendered":"
We propose to use the personalized speech synthesis and the neural language generator to synthesize content relevant personalized speech for rapid speaker adaptation. It has two distinct aspects: First, it relieves the general data sparsity issue in rapid adaptation via making use of additional synthesized personalized speech; Second, it circumvents the obstacle of the explicit 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