Learning Hidden Unit Contributions for Unsupervised Acoustic Model Adaptation
TABLE XIII: Results on Aurora 4. Multi-condition DNN model.
['Model', 'A', 'B', 'C', 'D', 'AVG']
but, as expected, a single transform estimated jointly on the target speaker and environment has a lower WER (12.4%). However, when interpolated with α=0.7 the result of the factorised model improves to 12.7% WER, although still higher that joint estimation. However, adaptation data for joint speaker-environment adaptation is not available in many scenarios, and the factorised adaptation based on interpolation is more flexible.