Maximum Likelihood Based Temporal Frame Selection

In this paper, we propose a maximum likelihood (ML) based frame selection approach. A fixed frame rate adopted in most state-of-the-art speech recognition systems can face some problems, such as accidentally meeting noisy frames, assigning the same importance to each frame, and pitch asynchronous representation. As an attempt to avoid those problems, our approach selects reliable frames from a fine resolution along the time axis in a phoneme recognition task, we show that significant improvements are achieved with the frame selection approach comparing to a system with a fixed frame rate