A study of broadcast news audio stream segmentation and segment clustering

Abstract

In transcription of broadcast news, dividing the signal into homogeneous segments, and clustering together similar segments is important. Decoding a complete broadcast news program in one chunk is technically di cult. Also, through creation of homogeneous clusters of segments, improvement from adaptation can be increased. Two systems of segmentation and clustering are compared. The best system used the BIC algorithm to produce long, homogeneous segments, and a nearest neighbour bottom-up agglomerative clustering algorithm to produce homogeneous clusters. Adaptation brought a word error rate (WER) improvement from 23:4% to 21:0% using the automatic segmentation and clustering, compared to an improvement from 21:8% to 20:0% using a handmade \correct segmentation and clustering.