Automatic Measurement of Pre-aspiration
This work addresses a domain-specific problem in linguistics for researchers analyzing speech patterns, but it is incremental as it builds on existing methods for acoustic measurement.
The authors tackled the problem of automatically measuring pre-aspiration duration in speech signals by proposing two machine learning methods, with the structured prediction model achieving higher accuracy and better generalization to new speakers and languages.
Pre-aspiration is defined as the period of glottal friction occurring in sequences of vocalic/consonantal sonorants and phonetically voiceless obstruents. We propose two machine learning methods for automatic measurement of pre-aspiration duration: a feedforward neural network, which works at the frame level; and a structured prediction model, which relies on manually designed feature functions, and works at the segment level. The input for both algorithms is a speech signal of an arbitrary length containing a single obstruent, and the output is a pair of times which constitutes the pre-aspiration boundaries. We train both models on a set of manually annotated examples. Results suggest that the structured model is superior to the frame-based model as it yields higher accuracy in predicting the boundaries and generalizes to new speakers and new languages. Finally, we demonstrate the applicability of our structured prediction algorithm by replicating linguistic analysis of pre-aspiration in Aberystwyth English with high correlation.