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Model adaptation is demonstrated and shown to provide process insight.
The improvement obtained using the acoustic model adaptation is relatively small.
On-line model adaptation is proposed as a possible approach to overcome the constant wave shape assumption.
The model adaptation is also shown to be crucial for achieving acceptable control performance when encountering large disturbances in the initial conditions.
To meet these requirements, we develop an evolving version of fuzzy pattern tree learning, in which model adaptation is realized by anticipating possible local changes of the current model, and confirming these changes through statistical hypothesis testing.
These results support our previous suggestion that model adaptation is more effective than feature compensation in serious noise conditions where it becomes more difficult to compensate noisy speech features into clean speech features due to the increased loss of acoustic-phonetic information.
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SGMM was better than GMM because model adaptation was well performed.
Third, if cross transform feature adaptation and the CMLLR model adaptation are applied in sequence, the WER is further reduced.
The system including the feedback loop for model adaptation was tested upon the detection of simulated atypical and typical situations.
Model adaptation was determined to be acceptable based on the following criteria: non-statistically significant difference in chi-square statistic, high CFI (> 0.95), high TLI (> 0.95), low RMSEA (< 0.06), and low SRMR (< 0.11) [ 25- 29].
True to Fisher's original presentation, the geometric model of adaptation is rarely visualised as an adaptive landscape despite the underlying conceptual similarities.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com