Content-based access to spoken audio

Abstract

The amount of archived audio material in digital form is increasing rapidly, as advantage is taken of the growth in available storage and processing power. Computational resources are becoming less of a bottleneck to digitally record and archive vast amounts of spoken material, both television and radio broadcasts and individual conversations. However, listening to this ever-growing amount of spoken audio sequentially is too slow, and the bottleneck will become the development of effective ways to access content in these voluminous archives. The provision of accurate and efficient computer-mediated content access is a challenging task, because spoken audio combines information from multiple levels (phonetic, acoustic, syntactic, semantic and discourse). Most systems that assist humans in accessing spoken audio content have approached the problem by performing automatic speech recognition, followed by text-based information access. These systems have addressed diverse tasks including indexing and retrieving voicemail messages, searching for broadcast news, and extracting information from recordings of meetings and lectures. Spoken audio content is far richer than what a simple textual transcription can capture as it has additional cues that disclose the intended meaning and speaker's emotional state. However, the text transcription alone still provides a great deal of useful information in applications. This article describes approaches to content-based access to spoken audio with a qualitative and tutorial emphasis. We describe how the analysis, retrieval and delivery phases contribute making spoken audio content more accessible, and we outline a number of outstanding research issues. We also discuss the main application domains and try to identify important issues for future developments. The structure of the article is based on general system architecture for content-based

Automatic Summarization

“…It provides an integrated overview of the techniques and technologies that are available to design and develop state-of-theart SCR systems. We bring together information from other overviews on the subject of searching speech [5,25,38,76,91,148,166,180,193] as well as from a large number of individual research papers. Our survey differs from other overviews in that it encompasses a broad range of application domains and is organized in terms of the overarching challenges that face SCR.…”

Broadcast news story segmentation using social network analysis and hidden markov models

“…to split the recording into segments which are meaningful from a user point of view [4]. In the case of broadcast news, the segmentation is typically performed in terms of stories, i.e.…”

Tools for Searching, Annotation and Analysis of Speech, Music, Film and Video A Survey

“…As far as possible, we have avoided addressing technical problems specific to single disciplines. Other recent publications have examined related issues of resources, access, and preservation (BUFVC, 2004;British Academy, 2005), though publications which deal also with issues of technology have generally focused only on speech (SWAG, 2003;Goldman et al, 2005;Koumpis & Renals, 2005;Lee & Chen 2005;Ostendorf et al, 2005).…”