HMM-based expressive speech synthesis based on phrase-level F0 context labeling

This paper proposes a technique for adding more prosodic variations to the synthetic speech in HMM-based expressive speech synthesis. We create novel phrase-level F0 context labels from the residual information of F0 features between original and synthetic speech for the training data. Specifically, we classify the difference of average log F0 values between the original and synthetic speech into three classes which have perceptual meanings, i.e., high, neutral, and low of relative pitch at the phrase level. We evaluate both ideal and practical cases using appealing and fairy tale speech recorded under a realistic condition. In the ideal case, we examine the potential of our technique to modify the F0 patterns under a condition where the original F0 contours of test sentences are known. In the practical case, we show how the users intuitively modify the pitch by changing the initial F0 context labels obtained from the input text.