Extracting Mel-Frequency and Bark-Frequency Cepstral Coefficients from Encrypted Signals

We describe a method for extracting Mel-Frequency and Bark-Frequency Cepstral Coefficient from an encrypted signal without having to decrypt any intermediate values. To do so, we introduce a novel approach for approximating the value of logarithms given encrypted input data. This method works over any interval for which logarithms are defined and bounded. Extracting spectral features from encrypted signals is the first step towards achieving secure end-to-end automatic speech recognition over encrypted data. We experimentally determine the appropriate precision thresholds to support accurate WER for ASR over the TIMIT dataset.