Generally audio news broadcast on radio is com- posed of music, commercials, news from correspondents and recorded statements in addition to the actual news read by the newsreader. When news transcripts are available, automatic segmentation of audio news broadcast to time align the audio with the text transcription to build frugal speech corpora is essential. We address the problem of identifying segmentation in the audio news broadcast corresponding to the news read by the newsreader so that they can be mapped to the text transcripts. The existing techniques produce sub-optimal solutions when used to extract newsreader read segments. In this paper, we propose a new technique which is able to identify the acoustic change points reliably using an acoustic Self Similarity Matrix (SSM). We describe the two pass technique in detail and verify its performance on real audio news broadcast of All India Radio for different languages.
Unsupervised Data Selection for TTS: Using Arabic Broadcast News as a Case Study
KANARI AI（卡纳里人工智能公司）；Carnegie Mellon University（卡内基梅隆大学）；Qatar Computing Research Institute, HBKU（哈马德·本·哈利法大学卡塔尔计算研究所）；Nagoya University（名古屋大学）；Human Dataware Lab. Co., Ltd.（人类数据仓库实验室有限公司）；American University of Beirut（贝鲁特美国大学）
From 2.1M Hours of Raw Broadcast Audio to Finnish Speech SSL: Confidence Filtering and Architectural Analysis at Scale
Aalto University（阿尔托大学）；Walton Institute, South East Technological University（东南理工大学沃尔顿研究所）；Finnish National Audiovisual Institute (KAVI)（芬兰国家视听研究所（KAVI））
Advancing Topic Segmentation of Broadcasted Speech with Multilingual Semantic Embeddings
Fraunhofer Institute for Digital Media Technology（弗劳恩霍夫数字媒体技术研究所）；Fraunhofer Institute for Intelligent Analysis and Information Systems（弗劳恩霍夫智能分析与信息系统研究所）
