  The historical and geographical spread from older to more modern languages
has long been studied by examining textual changes and in terms of changes in
phonetic transcriptions. However, it is more difficult to analyze language
change from an acoustic point of view, although this is usually the dominant
mode of transmission. We propose a novel analysis approach for acoustic
phonetic data, where the aim will be to statistically model the acoustic
properties of spoken words. We explore phonetic variation and change using a
time-frequency representation, namely the log-spectrograms of speech
recordings. We identify time and frequency covariance functions as a feature of
the language; in contrast, mean spectrograms depend mostly on the particular
word that has been uttered. We build models for the mean and covariances
(taking into account the restrictions placed on the statistical analysis of
such objects) and use these to define a phonetic transformation that models how
an individual speaker would sound in a different language, allowing the
exploration of phonetic differences between languages. Finally, we map back
these transformations to the domain of sound recordings, allowing us to listen
to the output of the statistical analysis. The proposed approach is
demonstrated using recordings of the words corresponding to the numbers from
"one" to "ten" as pronounced by speakers from five different Romance languages.
