In this paper, we propose a direct method for speech rate estimation from acoustic features without requiring any automatic speech transcription. We compare various spectral and temporal signal analysis and smoothing strategies to better characterize the underlying syllable structure to derive speech rate. The proposed algorithm extends the methods of spectral subband correlation by including temporal correlation and the use of prominent spectral subbands for improving the signal correlation essential for syllable detection. Furthermore, to address some of the practical robustness issues in previously proposed methods, we introduce some novel components into the algorithm such as the use of pitch confidence for filtering spurious syllable envelope peaks, magnifying window for tackling neighboring syllable smearing, and relative peak measure thresholds for pseudo peak rejection. We also describe an automated approach for learning algorithm parameters from data, and find the optimal settings through Monte Carlo simulations and parameter sensitivity analysis. Final experimental evaluations are conducted based on a portion of the Switchboard corpus for which manual phonetic segmentation information, and published results for direct comparison are available. The results show a correlation coefficient of 0.745 with respect to the ground truth based on manual segmentation. This result is about a 17% improvement compared to the current best single estimator and a 11% improvement over the multiestimator evaluated on the same Switchboard database.

An algorithm for automatic speech prominence detection is reported in this paper. We describe a comparative analysis on various acoustic features for word prominence detection and report results using a spoken dialog corpus with manually assigned prominence labels. The focus is on features such as spectral intensity and speech rate that are directly extracted from speech based on a correlation-based approach without requiring explicit linguistic or phonetic knowledge. Additionally, various pitch-based measures are studied with respect to their discriminating ability for prominence detection. A parametric scheme for modeling pitch plateau is proposed and this feature alone is found to outperform the traditional local pitch statistics. Two sets of experiments are used to explore the usefulness of the acoustic score generated using these features. The first set focuses on a more traditional way of word prominence detection based on a manually-tagged corpus. A 76.8% classification accuracy was achieved on a corpus of role-playing spoken dialogs. Due to difficulties in manually tagging speech prominence into discrete levels (categories), the second set of experiments focuses on evaluating the score indirectly. Specifically, through experiments on the Switchboard corpus, it is shown that the proposed acoustic score can discriminate between content word and function words in a statistically significant way. The relation between speech prominence and content/function words is also explored. Since prominent words tend to be predominantly content words, and since content words can be automatically marked from text-derived part of speech (POS) information, it is shown that the proposed acoustic score can be indirectly cross-validated through POS information.

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