Estimation and comparison of the glottal source waveform across stress styles using glottal inverse filtering

Abstract

An iterative method to extract the glottal waveform using inverse filtering is presented. The method is then applied to the analysis of glottal waveforms from utterances displaying eleven different styles of speech: normal, slow, fast, angry, loud, soft, clear, question, two different task loading conditions, and speech produced while the talker is presented with noise through headphones (Lombard effect). Results of the analysis are presented in terms of statistical descriptors of the extracted glottal waveforms for each style. Typical examples of the waveforms, displaying the salient features of each, are shown, and a qualitative discussion of the results is presented. It is concluded that this method of extracting glottal waveforms using inverse filtering, while time-consuming, gives reasonable results. The extracted waveforms are consistent across utterances, and changes in the glottal waveshape that theoretically should occur under different stress conditions are present in the extracted glottal waveforms.