Abstract
Conventional speech features, such as mel-frequency cepstral coefficients, tend to perform well in template matching systems, such as dynamic time warping, in low noise conditions. However, they tend to degrade in noisy environments. We propose a method of calculating features using the probabilistic latent component analysis (PLCA) framework. This framework models the speech and noise separately, leading to higher performance in noisy conditions than conventional methods. In this work, we compare our PLCA-based features with conventional features on the task of aligning a high-fidelity speech recording to a noisy speech recording, a scenario common in automatic dialogue replacement.
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