End-to-End Training of a Neural HMM with Label and Transition
Probabilities
- URL: http://arxiv.org/abs/2310.02724v1
- Date: Wed, 4 Oct 2023 10:56:00 GMT
- Title: End-to-End Training of a Neural HMM with Label and Transition
Probabilities
- Authors: Daniel Mann, Tina Raissi, Wilfried Michel, Ralf Schl\"uter, Hermann
Ney
- Abstract summary: We investigate a novel modeling approach for end-to-end neural network training using hidden Markov models (HMM)
In our approach there are explicit, learnable probabilities for transitions between segments as opposed to a blank label that implicitly encodes duration statistics.
We find that while the transition model training does not improve recognition performance, it has a positive impact on the alignment quality.
- Score: 36.32865468394113
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We investigate a novel modeling approach for end-to-end neural network
training using hidden Markov models (HMM) where the transition probabilities
between hidden states are modeled and learned explicitly. Most contemporary
sequence-to-sequence models allow for from-scratch training by summing over all
possible label segmentations in a given topology. In our approach there are
explicit, learnable probabilities for transitions between segments as opposed
to a blank label that implicitly encodes duration statistics. We implement a
GPU-based forward-backward algorithm that enables the simultaneous training of
label and transition probabilities. We investigate recognition results and
additionally Viterbi alignments of our models. We find that while the
transition model training does not improve recognition performance, it has a
positive impact on the alignment quality. The generated alignments are shown to
be viable targets in state-of-the-art Viterbi trainings.
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