  In this paper, we analyse the error patterns of the raw waveform acoustic
models in TIMIT's phone recognition task. Our analysis goes beyond the
conventional phone error rate (PER) metric. We categorise the phones into three
groups: {affricate, diphthong, fricative, nasal, plosive, semi-vowel, vowel,
silence}, {consonant, vowel+, silence}, and {voiced, unvoiced, silence} and,
compute the PER for each broad phonetic class in each category. We also
construct a confusion matrix for each category using the substitution errors
and compare the confusion patterns with those of the Filterbank and Wav2vec 2.0
systems. Our raw waveform acoustic models consists of parametric (Sinc2Net) or
non-parametric CNNs and Bidirectional LSTMs, achieving down to 13.7%/15.2% PERs
on TIMIT Dev/Test sets, outperforming reported PERs for raw waveform models in
the literature. We also investigate the impact of transfer learning from WSJ on
the phonetic error patterns and confusion matrices. It reduces the PER to
11.8%/13.7% on the Dev/Test sets.
