TY - JOUR
T1 - Story Segmentation and Topic Classification of Broadcast News via a Topic-Based Segmental Model and a Genetic Algorithm
AU - Wu, Chung Hsien
AU - Hsieh, Chia Hsin
N1 - Funding Information:
Manuscript received June 30, 2008; revised March 23, 2009. Current version published September 04, 2009. This work was supported by the National Science Council, Taiwan, under Contract 95-2221-E-006-181-MY3. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Gerhard Rigoll.
PY - 2009/11
Y1 - 2009/11
N2 - This paper presents a two-stage approach to story segmentation and topic classification of broadcast news. The two-stage paradigm adopts a decision tree and a maximum entropy model to identify the potential story boundaries in the broadcast news within a sliding window. The problem for story segmentation is thus transformed to the determination of a boundary position sequence from the potential boundary regions. A genetic algorithm is then applied to determine the chromosome, which corresponds to the final boundary position sequence. A topic-based segmental model is proposed to define the fitness function applied in the genetic algorithm. The syllable- and word-based story segmentation schemes are adopted to evaluate the proposed approach. Experimental results indicate that a miss probability of 0.1587 and a false alarm probability of 0.0859 are achieved for story segmentation on the collected broadcast news corpus. On the TDT-3 Mandarin audio corpus, a miss probability of 0.1232 and a false alarm probability of 0.1298 are achieved. Moreover, an outside classification accuracy of 74.55% is obtained for topic classification on the collected broadcast news, while an inside classification accuracy of 88.82% is achieved on the TDT-2 Mandarin audio corpus.
AB - This paper presents a two-stage approach to story segmentation and topic classification of broadcast news. The two-stage paradigm adopts a decision tree and a maximum entropy model to identify the potential story boundaries in the broadcast news within a sliding window. The problem for story segmentation is thus transformed to the determination of a boundary position sequence from the potential boundary regions. A genetic algorithm is then applied to determine the chromosome, which corresponds to the final boundary position sequence. A topic-based segmental model is proposed to define the fitness function applied in the genetic algorithm. The syllable- and word-based story segmentation schemes are adopted to evaluate the proposed approach. Experimental results indicate that a miss probability of 0.1587 and a false alarm probability of 0.0859 are achieved for story segmentation on the collected broadcast news corpus. On the TDT-3 Mandarin audio corpus, a miss probability of 0.1232 and a false alarm probability of 0.1298 are achieved. Moreover, an outside classification accuracy of 74.55% is obtained for topic classification on the collected broadcast news, while an inside classification accuracy of 88.82% is achieved on the TDT-2 Mandarin audio corpus.
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U2 - 10.1109/TASL.2009.2021304
DO - 10.1109/TASL.2009.2021304
M3 - Article
AN - SCOPUS:85008049757
SN - 1558-7916
VL - 17
SP - 1612
EP - 1623
JO - IEEE Transactions on Audio, Speech and Language Processing
JF - IEEE Transactions on Audio, Speech and Language Processing
IS - 8
ER -