Speaker Diarization Using Convolutional Neural Network for Statistics Accumulation Refinement

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F17%3A43932657" target="_blank" >RIV/49777513:23520/17:43932657 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.21437/Interspeech.2017-51" target="_blank" >http://dx.doi.org/10.21437/Interspeech.2017-51</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.21437/Interspeech.2017-51" target="_blank" >10.21437/Interspeech.2017-51</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Speaker Diarization Using Convolutional Neural Network for Statistics Accumulation Refinement

Popis výsledku v původním jazyce

The aim of this paper is to investigate the benefit of information from a speaker change detection system based on Convolutional Neural Network (CNN) when applied to the process of accumu- lation of statistics for an i-vector generation. The investigation is carried out on the problem of diarization. In our system, the output of the CNN is a probability value of a speaker change in a conversation for a given time segment. According to this probability, we cut the conversation into short segments that are then represented by the i-vector (to describe a speaker in it). We propose a technique to utilize the information from the CNN for the weighting of the acoustic data in a segment to refine the statistics accumulation process. This technique enables us to represent the speaker better in the final i-vector. The experi- ments on the English part of the CallHome corpus show that our proposed refinement of the statistics accumulation is beneficial with the relative improvement of Diarization Error Rate almost by 16 % when compared to the speaker diarization system with- out statistics refinement.

Název v anglickém jazyce

Speaker Diarization Using Convolutional Neural Network for Statistics Accumulation Refinement

Popis výsledku anglicky

The aim of this paper is to investigate the benefit of information from a speaker change detection system based on Convolutional Neural Network (CNN) when applied to the process of accumu- lation of statistics for an i-vector generation. The investigation is carried out on the problem of diarization. In our system, the output of the CNN is a probability value of a speaker change in a conversation for a given time segment. According to this probability, we cut the conversation into short segments that are then represented by the i-vector (to describe a speaker in it). We propose a technique to utilize the information from the CNN for the weighting of the acoustic data in a segment to refine the statistics accumulation process. This technique enables us to represent the speaker better in the final i-vector. The experi- ments on the English part of the CallHome corpus show that our proposed refinement of the statistics accumulation is beneficial with the relative improvement of Diarization Error Rate almost by 16 % when compared to the speaker diarization system with- out statistics refinement.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

<a href="/cs/project/LO1506" target="_blank" >LO1506: Podpora udržitelnosti centra NTIS - Nové technologie pro informační společnost</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 18th Annual Conference of the International Speech Communication Association (Interspeech 2017)

ISBN

978-1-5108-4876-4

ISSN

—

e-ISSN

—

Počet stran výsledku

5

Strana od-do

3562-3566

Název nakladatele

Curran Associates, Inc.

Místo vydání

Red Hook, NY

Místo konání akce

Stockholm, Sweden

Datum konání akce

20. 8. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000457505000741