A linked-HMM model for robust voicing and speech detection

Basu, Sumit

doi:10.1109/icassp.2003.1198906

Cited by 38 publications

(32 citation statements)

References 5 publications

(7 reference statements)

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“…During a social interaction, speech and silence operate as regulators of a conversation emitting social signals such as consensus, rejection and reveal interlocutors' social behaviour including their emotions [Koudenburg et al 2011]. One of the well-known and widely-used techniques to infer conversation existence was presented by Basu [Basu 2003]. It specified a linked Hidden Markov Model (HMM) with three features: non-initial maximum of the normalized noisy autocorrelation, number of autocorrelation peaks, and normalised spectral entropy.…”

Section: Auditorymentioning

confidence: 99%

A Survey on Mobile Social Signal Processing

et al. 2016

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Understanding human behaviour in an automatic but non-intrusive manner is an important area for various applications. This requires the collaboration of information technology with human sciences to transfer existing knowledge of human behaviour into self-acting tools. These tools will reduce human error that is introduced by current obtrusive methods such as questionnaires. To achieve unobtrusiveness, we focus on exploiting the pervasive and ubiquitous character of mobile devices.In this article, a survey of existing techniques for extracting social behaviour through mobile devices is provided. Initially we expose the terminology used in the area and introduce a concrete architecture for social signal processing applications on mobile phones, constituted by sensing, social interaction detection, behavioural cues extraction, social signal inference and social behaviour understanding. Furthermore, we present state-of-the-art techniques applied to each stage of the process. Finally, potential applications are shown while arguing about the main challenges of the area.

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Section: Auditorymentioning

confidence: 99%

A Survey on Mobile Social Signal Processing

et al. 2016

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“…Because of the close placement of the microphone with respect to the speaker's mouth we can use simple energy threshold to segment the speech from most of the other speech and ambient sounds. It is been shown that one can segment speech using voiced regions (speech regions that have pitch) alone [13]. In voiced regions energy is biased towards low-frequency range and hence we use low-energy threshold (2KHz cut off) instead of total energy.…”

Section: Data Analysis Methodsmentioning

confidence: 99%

“…Purely energy-based approach to speaker segmentation is potentially very susceptible to the noise level of the environment and sound from the user's regular activity. In order to overcome this problem we have incorporated robust speech features (non-initial maximum of the autocorrelation, the number of auto-correlation peaks and the normalized spectral entropy) proposed in [13]. An HMM trained to detect voiced/unvoiced regions using these features is very reliable even in noisy environment with less than 2% error at 10dB SSNR.…”

Section: Data Analysis Methodsmentioning

confidence: 99%

Sensing and modeling human networks using the sociometer

Choudhury¹,

Pentland²

Seventh IEEE International Symposium on Wearable Computers, 2003. Proceedings.

175

173

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“…Ambient sound and music have less phase deviation than the human voice. Finally, Relative Spectral Entropy (RSE), which is simply the KL (Kullback-Liebker) divergence between the current spectrum and the local mean spectrum ( [Basu, 2003]). It is calculated from sound signals in order to differentiate the human speech form other sounds.…”

Section: Features Used For Detecting Social Interactionsmentioning

confidence: 99%

A survey on smartphone-based systems for opportunistic user context recognition

2013

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The ever growing computation and storage capability of mobile phones has given rise to mobile centric context recognition systems, which are able to sense and analyze the context of the carrier so as to provide an appropriate level of service. Particularly, as nonintrusive autonomous sensing and context recognition are one of the most desirable characteristics of a personal sensing system; commendable efforts have been made to develop opportunistic sensing techniques on mobile phones. The resulting combination of these approaches has ushered in a new realm of applications, namely opportunistic user context recognition with mobile phones. This article surveys the existing research and approaches towards realization of such systems. In doing so, the typical architecture of a mobile centric user context recognition system as a sequential process of sensing, pre-processing and context recognition phases are introduced. The main techniques used for the realization of the respective processes during these phases are described, highlighting strengths and limitations of those. In addition, lessons learned from previous approaches are presented as motivation for future research. Finally, several open challenges are discussed as possible ways to extend the capabilities of current systems and improve their real-world experience.

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A linked-HMM model for robust voicing and speech detection

Cited by 38 publications

References 5 publications

A Survey on Mobile Social Signal Processing

A Survey on Mobile Social Signal Processing

Sensing and modeling human networks using the sociometer

A survey on smartphone-based systems for opportunistic user context recognition

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