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arXiv · 2210.09951

HMM vs. CTC for Automatic Speech Recognition: Comparison Based on Full-Sum Training from Scratch

Tina Raissi·Wei Zhou·Simon Berger·Ralf Schlüter·Hermann Ney
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Abstract

In this work, we compare from-scratch sequence-level cross-entropy (full-sum) training of Hidden Markov Model (HMM) and Connectionist Temporal Classification (CTC) topologies for automatic speech recognition (ASR). Besides accuracy, we further analyze their capability for generating high-quality time alignment between the speech signal and the transcription, which can be crucial for many subsequent applications. Moreover, we propose several methods to improve convergence of from-scratch full-sum training by addressing the alignment modeling issue. Systematic comparison is conducted on both Switchboard and LibriSpeech corpora across CTC, posterior HMM with and w/o transition probabilities, and standard hybrid HMM. We also provide a detailed analysis of both Viterbi forced-alignment and Baum-Welch full-sum occupation probabilities.

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Tina Raissi, Wei Zhou, Simon Berger, Ralf Schlüter, Hermann Ney. 2022-10-18. HMM vs. CTC for Automatic Speech Recognition: Comparison Based on Full-Sum Training from Scratch. https://arxiv.org/abs/2210.09951

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