| An acoustics-based approach | The upper panel of Figure 1 shows a matrix of frame-level similarity scores between these two utterances where lighter grey represents higher similarity. |
| An acoustics-based approach | All similarity scores are then normalized to the range of [0, l], which yields similarity matrices exemplified in the upper panel of Figure 1. |
| An acoustics-based approach | Given an M -by-N matrix of frame-level similarity scores , the top-left corner is considered the origin, and the bottom-right comer represents an alignment of the last frames in each sequence. |
| Introduction | Park-Glass similarity scores by themselves can attribute a high score to distorted paths that, in our context, ultimately leads to too many false-alarm alignments, even after applying the distortion threshold. |
| Related work | MEAD uses a redundancy removal mechanism similar to MMR, but to decide the salience of a sentence to the whole topic, MEAD uses not only its similarity score but also sentence position, e.g., the first sentence of each new story is considered important. |