Variational autoencoder for prosody-based speaker recognition
This paper describes a novel end-to-end deep generative model-based speaker recognition system using prosodic features. The usefulness of variational autoencoders (VAE) in learning the speaker-specific prosody representations for the speaker recognition task is examined herein for the first time. Th...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Electronics and Telecommunications Research Institute (ETRI)
2023-08-01
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| Series: | ETRI Journal |
| Subjects: | |
| Online Access: | https://doi.org/10.4218/etrij.2021-0377 |
| _version_ | 1797692853909979136 |
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| author | Starlet Ben Alex Leena Mary |
| author_facet | Starlet Ben Alex Leena Mary |
| author_sort | Starlet Ben Alex |
| collection | DOAJ |
| description | This paper describes a novel end-to-end deep generative model-based speaker recognition system using prosodic features. The usefulness of variational autoencoders (VAE) in learning the speaker-specific prosody representations for the speaker recognition task is examined herein for the first time. The speech signal is first automatically segmented into syllable-like units using vowel onset points (VOP) and energy valleys. Prosodic features, such as the dynamics of duration, energy, and fundamental frequency (F0), are then extracted at the syllable level and used to train/adapt a speaker-dependent VAE from a universal VAE. The initial comparative studies on VAEs and traditional autoencoders (AE) suggest that the former can efficiently learn speaker representations. Investigations on the impact of gender information in speaker recognition also point out that gender-dependent impostor banks lead to higher accuracies. Finally, the evaluation on the NIST SRE 2010 dataset demonstrates the usefulness of the proposed approach for speaker recognition. |
| first_indexed | 2024-03-12T02:33:36Z |
| format | Article |
| id | doaj.art-385e84b5cef94c2a97de5507e46ef1f5 |
| institution | Directory Open Access Journal |
| issn | 1225-6463 |
| language | English |
| last_indexed | 2024-03-12T02:33:36Z |
| publishDate | 2023-08-01 |
| publisher | Electronics and Telecommunications Research Institute (ETRI) |
| record_format | Article |
| series | ETRI Journal |
| spelling | doaj.art-385e84b5cef94c2a97de5507e46ef1f52023-09-05T01:46:15ZengElectronics and Telecommunications Research Institute (ETRI)ETRI Journal1225-64632023-08-0145467868910.4218/etrij.2021-037710.4218/etrij.2021-0377Variational autoencoder for prosody-based speaker recognitionStarlet Ben AlexLeena MaryThis paper describes a novel end-to-end deep generative model-based speaker recognition system using prosodic features. The usefulness of variational autoencoders (VAE) in learning the speaker-specific prosody representations for the speaker recognition task is examined herein for the first time. The speech signal is first automatically segmented into syllable-like units using vowel onset points (VOP) and energy valleys. Prosodic features, such as the dynamics of duration, energy, and fundamental frequency (F0), are then extracted at the syllable level and used to train/adapt a speaker-dependent VAE from a universal VAE. The initial comparative studies on VAEs and traditional autoencoders (AE) suggest that the former can efficiently learn speaker representations. Investigations on the impact of gender information in speaker recognition also point out that gender-dependent impostor banks lead to higher accuracies. Finally, the evaluation on the NIST SRE 2010 dataset demonstrates the usefulness of the proposed approach for speaker recognition.https://doi.org/10.4218/etrij.2021-0377deep neural networksprosodic featuresspeaker recognitionsyllablesvae |
| spellingShingle | Starlet Ben Alex Leena Mary Variational autoencoder for prosody-based speaker recognition ETRI Journal deep neural networks prosodic features speaker recognition syllables vae |
| title | Variational autoencoder for prosody-based speaker recognition |
| title_full | Variational autoencoder for prosody-based speaker recognition |
| title_fullStr | Variational autoencoder for prosody-based speaker recognition |
| title_full_unstemmed | Variational autoencoder for prosody-based speaker recognition |
| title_short | Variational autoencoder for prosody-based speaker recognition |
| title_sort | variational autoencoder for prosody based speaker recognition |
| topic | deep neural networks prosodic features speaker recognition syllables vae |
| url | https://doi.org/10.4218/etrij.2021-0377 |
| work_keys_str_mv | AT starletbenalex variationalautoencoderforprosodybasedspeakerrecognition AT leenamary variationalautoencoderforprosodybasedspeakerrecognition |