Wyniki wyszukiwania dla: SPEAKER RECOGNITION

• Speaker Recognition Using Convolutional Neural Network with Minimal Training Data for Smart Home Solutions

  Publikacja

  • M. Wang
  • T. Sirlapu
  • A. Kwaśniewska
  • M. Szankin
  • M. Bartscherer
  • R. Nicolas

  - Rok 2018

  With the technology advancements in smart home sector, voice control and automation are key components that can make a real difference in people's lives. The voice recognition technology market continues to involve rapidly as almost all smart home devices are providing speaker recognition capability today. However, most of them provide cloud-based solutions or use very deep Neural Networks for speaker recognition task, which are...

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• Bożena Kostek prof. dr hab. inż.

  Osoby

  Laboratorium Akustyki Fonicznej

• Andrzej Czyżewski prof. dr hab. inż.

  Osoby

  Katedra Systemów Multimedialnych

  Prof. zw. dr hab. inż. Andrzej Czyżewski jest absolwentem Wydziału Elektroniki PG (studia magisterskie ukończył w 1982 r.). Pracę doktorską na temat związany z dźwiękiem cyfrowym obronił z wyróżnieniem na Wydziale Elektroniki PG w roku 1987. W 1992 r. przedstawił rozprawę habilitacyjną pt.: „Cyfrowe operacje na sygnałach fonicznych”. Jego kolokwium habilitacyjne zostało przyjęte jednomyślnie w czerwcu 1992 r. w Akademii Górniczo-Hutniczej...

• Application of dynamic time warping and cepstrograms to text-dependent speaker verification

  Publikacja

  • A. Kaczmarek
  • M. Staworko

  - Rok 2009

  This work provides a description of an automatic speaker verification (ASV) system. In particular, it documents the evolution of all individual stages of the proposed ASV system design from the phase of preprocessing to an operational decision making system. The aim of this research was to achieve the system of the best safety and ease of use in view of users. The objective estimation of this target has been accomplished by assessing...

• Open-Set Speaker Identification Using Closed-Set Pretrained Embeddings

  Publikacja

  • M. Affek
  • M. S. Tatara

  - Rok 2022

  The paper proposes an approach for extending deep neural networks-based solutions to closed-set speaker identification toward the open-set problem. The idea is built on the characteristics of deep neural networks trained for the classification tasks, where there is a layer consisting of a set of deep features extracted from the analyzed inputs. By extracting this vector and performing anomaly detection against the set of known...

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• Analysis of the harmonic structure of the vowel /a/ taking into account the age and gender of the speaker

  Publikacja

  • S. Drywa

  - Rok 2024

  Sound waves are disturbances propagating through an elastic medium that, upon reaching the ear, elicit auditory sensations. Sounds generated by the surroundings can be captured by a transducer (microphone), which transforms them into an electrical signal. The signal from the microphone is then transmitted to a computer, where software allows for the extraction and analysis of individual tones. This process enables the description...

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• Spenser Studies

  Czasopisma

  ISSN: 0195-9468

• Emotion Recognition

  Dane Badawcze

  open access

  • M. Przyborski
  • K. Bobkowska

  - seria: Person A

  The films presented here were recorded using so-called high-speed camera Phantom Miro. To play the movie  You need the special software which can be downloaded from the web site https://www.phantomhighspeed.com/resourcesandsupport/phantomresources/pccsoftware the details of the movie are available after starting the movie in the viewer in the description...

• Emotion Recognition

  Dane Badawcze

  open access

  • M. Przyborski
  • K. Bobkowska

  - seria: Person A

  The films presented here were recorded using so-called high-speed camera Phantom Miro. To play the movie  You need the special software which can be downloaded from the web site https://www.phantomhighspeed.com/resourcesandsupport/phantomresources/pccsoftware the details of the movie are available after starting the movie in the viewer in the description...

• Edyta Gołąb-Andrzejak dr hab.

  Osoby

  Katedra Marketingu