Defining a Global Adaptive Duration Target Cost for Unit Selection Speech Synthesis

Unit selection speech synthesis systems generally rely on target and concatenation costs for selecting the best unit sequence. The role of the concatenation cost is to insure that joining two voice segments will not cause any acoustic artefact to appear. For this task, acoustic distances (MFCC, F0) are typically used but in many cases, this is not enough to prevent concatenation artefacts. Among other strategies, the improvement of corpus covering by favoring units that naturally support well the joining process (vocalic sandwiches) seems to be effective on TTS. In this paper, we investigate if vocalic sandwiches can be used directly in the unit selection engine when the corpus was not created using that principle. First, the sandwich approach is directly transposed in the unit selection engine with a penalty that greatly favors concatenation on sandwich boundaries. Second, a derived fuzzy version is proposed to relax the penalty based on the concatenation cost, with respect to the cost distribution. We show that the sandwich approach, very efficient at the corpus creation step, seems to be inefficient when directly transposed in the unit selection engine. However, we observe that the fuzzy approach enhances synthesis quality, especially on sentences with high concatenation costs.

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“…First though, we briefly present the TTS system used to implement the new costs. For a detailed description, refer to [17,18,19].…”

Section: Sandwiches In a Unit Selection Enginementioning

confidence: 99%

“…One might argue this is not optimal, but by experience, more refined tuning doesn't prove to be better. The set of filters we use in this work is the same as in [18].…”

Section: The Irisa Tts Systemmentioning

confidence: 99%