Speech rate estimation via temporal correlation and selected sub-band correlation
In this paper, we propose a novel method for speech rate estimation without requiring automatic speech recognition. It extends the methods of spectral subband correlation by including temporal correlation and the use of selecting prominent spectral subbands for correlation. Furthermore, to address some of the practical issues in previously published methods, we introduce some novel components into the algorithm such as the use of pitch confidence, magnifying window, relative peak measure and relative threshold. By selecting the parameters and thresholds from realistic development sets, this method achieves a 0.972 correlation coefficient on syllable number estimation and a 0.706 correlation on speech rate estimation. This result is about 6.9% improvement over the current best single estimator and 3.5% improvement over the current multi-estimator, evaluated on the same switchboard database.