Previous work has shown that the energy components of frequency subbands with a variety of frequencies and bandwidths predict pitch accent with various degrees of accuracy, and produce correct predictions for distinct subsets of data points. In this paper, we describe a series of experiments exploring techniques to leverage the predictive power of these energy components by including pitch and duration features – other known correlates to pitch accent. We perform these experiments on Standard American English read, spontaneous and broadcast news speech, each corpus containing at least four speakers. Using an approach by which we correct energy-based predictions using pitch and duration information prior to using a majority voting classifier, we were able to detect pitch accent in read, spontaneous and broadcast news speech at 84.0%, 88.3 % and 88.5 % accuracy, respectively. Human performance at pitch accent detection is generally taken to be between 85 % and 90%.
展开▼
