CARL+PCEN, implemented in the CarlFrontend library, is our frontend for performing phonetic analysis. It begins with the Cascade of Asymmetric Resonators, Linear (CARL). CARL is a bandpass filterbank and a linear auditory filter model with an efficient cascaded structure. The CARL filterbank is followed by per-channel energy normalization (PCEN) compression. We use the PCEN-compressed energies as input for 2-D vowel space embedding.
Example frontend output ("Ok Google, make me a sandwich"):
CARL is an efficient linear filterbank of bandpass channels on an auditory frequency scale, with spacing to the next channel proportional to the auditory bandwidth at each channel. Channels are in decreasing frequency order.
The CARL filterbank is implemented as a cascade of biquad filter stages. The resonant frequency decreases with each stage. The kth channel is formed by taking the output after (k+1) stages, then applying a difference filter. For efficiency, later stages of the cascade decimate the signal to successively lower sample rates where possible.
After the CARL filterbank, we convert the bandpass outputs to energies by half-wave rectification and squaring, then lowpass filter to obtain smooth energy envelopes. The energy envelope for each channel is then compressed with PCEN,
pcen_output = (envelope / (γ + pcen_denom)^α + δ)^β − δ^β,
where pcen_denom is a lowpassed version of envelope and by default
α = 0.7, β = 0.2, γ = 10−12, δ = 0.001.
PCEN is a robust alternative to the log compression that is typically done in speech frontends. We have previously used PCEN successfully in keyword spotting,
Yuxuan Wang, Pascal Getreuer, Thad Hughes, Richard F. Lyon, Rif A. Saurous, "Trainable frontend for robust and far-field keyword spotting." In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5670-5674. IEEE, 2017.