Exploring Hybrid CTC/Attention End-to-End Speech Recognition with Gaussian Processes

Abstract

Hybrid CTC/attention end-to-end speech recognition combines two powerful concepts. Given a speech feature sequence, the attention mechanism directly outputs a sequence of letters. Connectionist Temporal Classification (CTC) helps to bind the attention mechanism to sequential alignments. This hybrid architecture also gives more degrees of freedom in choosing parameter configurations. We applied Gaussian process optimization to estimate the impact of network parameters and language model weight in decoding towards Character Error Rate (CER), as well as attention accuracy. In total, we trained 70 hybrid CTC/attention networks and performed 590 beam search runs with an RNNLM as language model on the TEDlium v2 test set. To our surprise, the results challenge the assumption that CTC primarily regularizes the attention mechanism. We argue in an evidence-based manner that CTC instead regularizes the impact of language model feedback in a one-pass beam search, as letter hypotheses are fed back into the attention mechanism. Attention-only models without RNNLM already achieved \(10.9\%\) CER, or \(22.4\%\) Word Error Rate (WER), on the TEDlium v2 test set. Combined decoding of same attention-only networks with RNNLM strongly underperformed, with at best \(40.2\%\) CER, or, \(49.3\%\) WER. A combined hybrid CTC/attention model with RNNLM performed best, with \(8.9\%\) CER, or \(17.6\%\) WER.