Processing of speech signals directly in the time
domain is commonly regarded to be difficult and unstable, due to fact
that perceptually very similar utterances exhibit very large
variability in their raw waveforms. As a result, by far the most common
preprocessing step for most speech systems is to convert the raw
waveform into a time-frequency representation, using a variety of
spectral analysis and filterbank techniques.
In this work, we pursue a purely time domain approach to speech
processing in which we identify the samples at the boundaries between
glottal pulse periods (in voiced speech) or at the boundaries between
unvoiced segments. The computational challenge with this approach is in
efficiently and robustly identifying the segment boundaries, across
silence, unvoiced and voiced segments. We introduce a segmental Hidden
Markov Model, defined on variable length sections of the time domain
waveform, and show that performing inference in this model allows us to
identify segment boundaries. Once these segment boundaries are
identified, we can perform a variety of important low level speech
analysis operations directly and conveniently. For example,
timescale modification without pitch or format distortion can be
achieved by stochastically eliminating or replicating segments in the
time domain directly.