Advancing CTC Models for Better Speech Alignment: A Topological Approach

Automatic Speech Recognition (ASR) systems often face challenges in alignment quality, particularly with the Connectionist Temporal Classification (CTC) approach, which frequently results in a high number of blank frames, known as the “peaky” issue. In this study, we explore the impact of modifying ASR model topologies on alignment quality without compromising Word Error Rate (WER) performance. Our findings demonstrate that introducing additional states to the CTC topology significantly improves alignment quality and mitigates the peaky issue. Conversely, increasing the minimum traversal frame can degrade alignment quality in our specific settings. These insights emphasise the critical importance of topology design in balancing alignment accuracy and recognition performance in ASR systems.