Motivated by linguistic theories of prosodic categoricity, symbolic representations of prosody have recently attracted the attention of speech technologists. Categorical representations such as ToBI not only bear linguistic relevance, but also have the advantage that they can be easily modeled and integrated within applications. Since manual labeling of these categories is time-consuming and expensive, there has been signi cant interest in automatic prosody labeling. This paper presents a ne-grained ToBI-style prosody labeling system that makes use of features derived from RFC and TILT parameterization of F0 together with a n-gram prosodic language model for 4-way pitch accent labeling and 2-way boundary tone labeling. For this task, our system achieves pitch accent labeling accuracy of 56.4% and boundary tone labeling accuracy of 67.7% on the Boston University Radio News Corpus.
Narayanan et al. proposed a system that used features derived from the Rise-Fall-Connection (RFC) model of pitch with an -gram prosodic language model for four-way pitch accent labeling REF .
Fine-grained pitch accent and boundary tone labeling with parametric F0 features
2008 IEEE International Conference on Acoustics, Speech and Signal Processing
2008 IEEE International Conference on Acoustics, Speech and Signal Processing
Computer Science Medicine