On the Usage of Phonetic Information for Text-independent Speaker Embedding Extraction

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F19%3APU134177" target="_blank" >RIV/00216305:26230/19:PU134177 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.isca-speech.org/archive/Interspeech_2019/pdfs/3036.pdf" target="_blank" >https://www.isca-speech.org/archive/Interspeech_2019/pdfs/3036.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.21437/Interspeech.2019-3036" target="_blank" >10.21437/Interspeech.2019-3036</a>

Jazyk výsledku

angličtina

Název v původním jazyce

On the Usage of Phonetic Information for Text-independent Speaker Embedding Extraction

Popis výsledku v původním jazyce

Embeddings extracted by deep neural networks have become the state-of-the-art utterance representation in speaker recognition systems. It has recently been shown that incorporating frame-level phonetic information in the embedding extractor can improve the speaker recognition performance. On the other hand, in the final embedding, phonetic information is just an additional source of session variability which may be harmful to the text-independent speaker recognition task. This suggests that at the embedding level phonetic information should be suppressed rather than encouraged. To verify this hypothesis, we perform several experiments that encourage or/and suppress phonetic information at various stages in the network. Our experiments confirm that multitask learning is beneficial if it is applied at the frame-level stage of the network, whereas adversarial training is beneficial if it is used at the segment-level stage of the network. Additionally, the combination of these two approaches improves the performance further, resulting in an equal error rate of 3.17% on the VoxCeleb dataset.

Název v anglickém jazyce

On the Usage of Phonetic Information for Text-independent Speaker Embedding Extraction

Popis výsledku anglicky

Embeddings extracted by deep neural networks have become the state-of-the-art utterance representation in speaker recognition systems. It has recently been shown that incorporating frame-level phonetic information in the embedding extractor can improve the speaker recognition performance. On the other hand, in the final embedding, phonetic information is just an additional source of session variability which may be harmful to the text-independent speaker recognition task. This suggests that at the embedding level phonetic information should be suppressed rather than encouraged. To verify this hypothesis, we perform several experiments that encourage or/and suppress phonetic information at various stages in the network. Our experiments confirm that multitask learning is beneficial if it is applied at the frame-level stage of the network, whereas adversarial training is beneficial if it is used at the segment-level stage of the network. Additionally, the combination of these two approaches improves the performance further, resulting in an equal error rate of 3.17% on the VoxCeleb dataset.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

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

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of Interspeech

ISBN

—

ISSN

1990-9772

e-ISSN

—

Počet stran výsledku

5

Strana od-do

1148-1152

Název nakladatele

International Speech Communication Association

Místo vydání

Graz

Místo konání akce

INTERSPEECH 2019

Datum konání akce

15. 9. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000831796401061