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Noise in HMM-Based Speech Synthesis Adaptation: Analysis, Evaluation Methods and Experiments

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3 Author(s)
Karhila, R. ; Dept. of Signal Process. & Acoust., Aalto Univ., Aalto, Finland ; Remes, U. ; Kurimo, M.

This work describes experiments on using noisy adaptation data to create personalized voices with HMM-based speech synthesis. We investigate how environmental noise affects feature extraction and CSMAPLR and EMLLR adaptation. We investigate effects of regression trees and data quantity and test noise-robust feature streams for alignment and NMF-based source separation as preprocessing. The adaptation performance is evaluated using a listening test developed for noisy synthesized speech. The evaluation shows that speaker-adaptive HMM-TTS system is robust to moderate environmental noise.

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Selected Topics in Signal Processing, IEEE Journal of  (Volume:8 ,  Issue: 2 )