Novel Approaches to Speech Detection in the Processing of Continuous Audio Streams

Abstract

With the increasing amount of information stored in various audio-data documents there is a growing need for the efficient and effective processing, archiving and accessing of this information. One of the largest sources of such information is spoken audio documents, including broadcast-news (BN) shows, voice mails, recorded meetings, telephone conversations, etc. In these documents the information is mainly relayed through speech, which needs to be appropriately processed and analysed by applying automatic speech and language technologies. Spoken audio documents are produced by a wide range of people in a variety of situations, and are derived from various multimedia applications. They are usually collected as continuous audio streams and consist of multiple audio sources. These audio sources may be different speakers, music segments, types of noise, etc. For example, a BN show typically consists of speech from different speakers as well as music segments, commercials and various types of noises that are present in the background of the reports. In order to efficiently process or extract the required information from such documents the appropriate audio data need to be selected and properly prepared for further processing. In the case of speech-processing applications this means detecting just the speech parts in the audio data and delivering them as inputs in a suitable format for further speech processing. The detection of such speech segments in continuous audio streams and the segmentation of audio streams into either detected speech or non-speech data is known as the speech/nonspeech (SNS) segmentation problem. In this chapter we present an overview of the existing approaches to SNS segmentation in continuous audio streams and propose a new representation of audio signals that is more suitable for robust speech detection in SNSsegmentation systems. Since speech detection is usually applied as a pre-processing step in various speech-processing applications we have also explored the impact of different SNSsegmentation approaches on a speaker-diarisation task in BN data. This chapter is organized as follows: In Section 2 a new high-level representation of audio signals based on phoneme-recognition features is introduced. First of all we give a short overview of the existing audio representations used for speech detection and provide the basic ideas and motivations for introducing a new representation of audio signals for SNS segmentation. In the remainder of the section we define four features based on consonantvowel pairs and the voiced-unvoiced regions of signals, which are automatically detected by