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Robust speaking rate estimation can be useful in automatic speech recognition and speaker identification, and accurate, automatic measures of speaking rate are also relevant for research in linguistics, psychology, and social sciences. In this study we built a broad phonetic class recognizer for speaking rate estimation. We tested the recognizer on a variety of data sets, including laboratory speech, telephone conversations, foreign accented speech, and speech in different languages, and we found that the recognizer's estimates are robust under these sources of variation. We also found that the acoustic models of the broad phonetic classes are more robust than those of the monophones for syllable detection.