2004Unpublished venueRequires access

Measuring the perceived importance of time- and frequency-divided speech blocks for transmitting over packet networks

Akitoshi Kataoka, Yusuke Hiwasaki, Toru Morinaga, J. Ikedo

Open publisher page 1 citations

Abstract

This paper presents a way to calculate the perceived importance of speech segments as a single value criterion, using a linear regression model. Unlike the commonly used voice activity detection (VAD) algorithms, this method allows us to obtain a finer priority granularity of speech segments. This can be used in conjunction with frequency scalable speech coding techniques and IP QoS techniques to achieve efficient and qualitycontrolled voice transmission. A simple linear regression model is used to calculate the estimated mean opinion score (MOS) of the various cases of missing speech segments.

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What this paper is about

This paper presents a way to calculate the perceived importance of speech segments as a single value criterion, using a linear regression model. Unlike the commonly used voice activity detection (VAD) algorithms, this method allows us to obtain a finer priority granularity of speech segments. This can be used in conjunction with frequency scalable speech coding techniques and IP QoS techniques to achieve efficient and qualitycontrolled voice transmission. A simple linear regression model is used to calculate the estimated mean opinion score (MOS) of the various cases of missing speech segments.

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Available abstract

This paper presents a way to calculate the perceived importance of speech segments as a single value criterion, using a linear regression model. Unlike the commonly used voice activity detection (VAD) algorithms, this method allows us to obtain a finer priority granularity of speech segments. This can be used in conjunction with frequency scalable speech coding techniques and IP QoS techniques to achieve efficient and qualitycontrolled voice transmission. A simple linear regression model is used to calculate the estimated mean opinion score (MOS) of the various cases of missing speech segments.

Key concepts: Mean opinion score, Computer science, Voice activity detection, Linear predictive coding, Speech coding, Speech recognition, Granularity, Linear prediction

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