Speaker localization for microphone array-based ASR

Maganti, Hari Krishna; Gática-Pérez, Daniel

doi:10.1145/1180995.1181004

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Cited by 12 publications

(2 citation statements)

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“…The Amazon Echo device, for example, contains seven microphones. Using more than one microphone enables more accurate discrimination, because time lags between audio signals are used to separate speaker identities 28. Details such as avoiding contact between lapel microphones and clothes would help to reduce background noise and improve sound quality.…”

Section: Technical Barriersmentioning

confidence: 99%

Using artificial intelligence to assess clinicians’ communication skills

Ryan

Luz

Albert

et al. 2019

BMJ

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Section: Technical Barriersmentioning

confidence: 99%

Using artificial intelligence to assess clinicians’ communication skills

Ryan

Luz

Albert

et al. 2019

BMJ

View full text Add to dashboard Cite

show abstract

“…Furthermore, localization and tracking of active speakers from multiple far-field microphones are challenging tasks in smart room scenarios, where the speech signal is corrupted with noise from presentation devices and room reverberations (Maganti & Perez, 2006).…”

mentioning

confidence: 99%

Automatic Speaker Localization and Tracking

Ouamour

Sayoud

Khennouf

2010

International Journal of Mobile Computing and Multimedia Communications

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This paper presents a system of speaker localization for a purpose of speaker tracking by camera. The authors use the information given by the two microphones, placed in opposition, to determine the position of the active speaker in trying to supervise the audio-visual recording. To achieve the speaker localization task, the authors have proposed and employed two methods, which are called respectively: the filtered correlation method and the energy differential method. The principle of the first method is based on the calculation of the correlation between the two signals collected by the two microphones and a special filtering. The second is based on the computation of the logarithmic energy differential between these two signals. However, when different methods are used simultaneously to make a decision, it is often interesting to use a fusion technique combining those estimations or decisions in order to enhance the system performances. For that purpose, this paper proposes two fusion techniques operating at the decision level which are used to fuse the two estimations into one that should be more precise.

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Computer-Supported Human-Human Multilingual Communication

Waibel

Bernardin²,

Wölfel³

50 Years of Artificial Intelligence

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Speaker localization for microphone array-based ASR

Cited by 12 publications

References 10 publications

Using artificial intelligence to assess clinicians’ communication skills

Using artificial intelligence to assess clinicians’ communication skills

Automatic Speaker Localization and Tracking

Computer-Supported Human-Human Multilingual Communication

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