|
| | | | |
| | | |
| Stat |
Members: 3665
Articles: 2'599'751
Articles rated: 2609
23 January 2025 |
|
| | | | |
|
Article overview
| |
|
|
Analysing Discrete Self Supervised Speech Representation for Spoken Language Modeling | | Amitay Sicherman
; Yossi Adi
; | | Date: |
2 Jan 2023 | | Abstract: | This work profoundly analyzes discrete self-supervised speech representations
through the eyes of Generative Spoken Language Modeling (GSLM). Following the
findings of such an analysis, we propose practical improvements to the discrete
unit for the GSLM. First, we start comprehending these units by analyzing them
in three axes: interpretation, visualization, and resynthesis. Our analysis
finds a high correlation between the speech units to phonemes and phoneme
families, while their correlation with speaker or gender is weaker.
Additionally, we found redundancies in the extracted units and claim that one
reason may be the units’ context. Following this analysis, we propose a new,
unsupervised metric to measure unit redundancies. Finally, we use this metric
to develop new methods that improve the robustness of units clustering and show
significant improvement considering zero-resource speech metrics such as ABX.
Code and analysis tools are available under the following link. | | Source: | arXiv, 2301.00591 | | Services: | Forum | Review | PDF | Favorites | |
|
|
No review found.
Did you like this article?
Note: answers to reviews or questions about the article must be posted in the forum section.
Authors are not allowed to review their own article. They can use the forum section.
|
| |
|
|
|
REGISTER