| 1 |
RETRIEVING SPEAKER INFORMATION FROM PERSONALIZED ACOUSTIC MODELS FOR SPEECH RECOGNITION
|
|
|
|
In: IEEE ICASSP 2022 ; https://hal.archives-ouvertes.fr/hal-03539741 ; IEEE ICASSP 2022, 2022, Singapour, Singapore (2022)
|
|
Abstract:
International audience ; The widespread of powerful personal devices capable of collecting voice of their users has opened the opportunity to build speaker adapted speech recognition system (ASR) or to participate to collaborative learning of ASR. In both cases, personalized acoustic models (AM), i.e. fine-tuned AM with specific speaker data, can be built. A question that naturally arises is whether the dissemination of personalized acoustic models can leak personal information. In this paper, we show that it is possible to retrieve the gender of the speaker, but also his identity, by just exploiting the weight matrix changes of a neural acoustic model locally adapted to this speaker. Incidentally we observe phenomena that may be useful towards explainability of deep neural networks in the context of speech processing. Gender can be identified almost surely using only the first layers and speaker verification performs well when using middle-up layers. Our experimental study on the TED-LIUM 3 dataset with HMM/TDNN models shows an accuracy of 95% for gender detection, and an Equal Error Rate of 9.07% for a speaker verification task by only exploiting the weights from personalized models that could be exchanged instead of user data.
|
|
Keyword:
[INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; acoustic model; Automatic speech recognition; collaborative learning; personalized acoustic models; speaker information
|
|
URL: https://hal.archives-ouvertes.fr/hal-03539741
https://hal.archives-ouvertes.fr/hal-03539741/document
https://hal.archives-ouvertes.fr/hal-03539741/file/ICASSP_2022_SpeakerAnalysisInfoPrivacyVF.pdf
|
|
BASE
|
|
Hide details
|
|
|
| 2 |
From FreEM to D'AlemBERT ; From FreEM to D'AlemBERT: a Large Corpus and a Language Model for Early Modern French
|
|
|
|
In: Proceedings of the 13th Language Resources and Evaluation Conference ; https://hal.inria.fr/hal-03596653 ; Proceedings of the 13th Language Resources and Evaluation Conference, European Language Resources Association, Jun 2022, Marseille, France (2022)
|
|
BASE
|
|
Show details
|
|
|
| 3 |
Le modèle Transformer: un « couteau suisse » pour le traitement automatique des langues
|
|
|
|
In: Techniques de l'Ingenieur ; https://hal.archives-ouvertes.fr/hal-03619077 ; Techniques de l'Ingenieur, Techniques de l'ingénieur, 2022, ⟨10.51257/a-v1-in195⟩ ; https://www.techniques-ingenieur.fr/base-documentaire/innovation-th10/innovations-en-electronique-et-tic-42257210/transformer-des-reseaux-de-neurones-pour-le-traitement-automatique-des-langues-in195/ (2022)
|
|
BASE
|
|
Show details
|
|
|
| 4 |
Imputing Out-of-Vocabulary Embeddings with LOVE Makes Language Models Robust with Little Cost
|
|
|
|
In: ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-03613101 ; ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics, May 2022, Dublin, Ireland (2022)
|
|
BASE
|
|
Show details
|
|
|
| 5 |
Imputing out-of-vocabulary embeddings with LOVE makes language models robust with little cost
|
|
|
|
In: ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-03613101 ; ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics, May 2022, Dublin, Ireland (2022)
|
|
BASE
|
|
Show details
|
|
|
| 6 |
Structured, flexible, and robust: comparing linguistic plans and explanations generated by humans and large language models ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 7 |
On the Transferability of Pre-trained Language Models for Low-Resource Programming Languages ...
|
|
Chen, Fuxiang. - : Federated Research Data Repository / dépôt fédéré de données de recherche, 2022
|
|
BASE
|
|
Show details
|
|
|
| 8 |
Sentence Level Embedding Detoxification via Toxic Component Removal ...
|
|
: University of Virginia, 2022
|
|
BASE
|
|
Show details
|
|
|
| 9 |
MIss RoBERTa WiLDe: Metaphor Identification Using Masked Language Model with Wiktionary Lexical Definitions
|
|
|
|
In: Applied Sciences; Volume 12; Issue 4; Pages: 2081 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 10 |
Considering Commonsense in Solving QA: Reading Comprehension with Semantic Search and Continual Learning
|
|
|
|
In: Applied Sciences; Volume 12; Issue 9; Pages: 4099 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 11 |
Analysis of the Full-Size Russian Corpus of Internet Drug Reviews with Complex NER Labeling Using Deep Learning Neural Networks and Language Models
|
|
|
|
In: Applied Sciences; Volume 12; Issue 1; Pages: 491 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 12 |
Commonsense Knowledge-Aware Prompt Tuning for Few-Shot NOTA Relation Classification
|
|
|
|
In: Applied Sciences; Volume 12; Issue 4; Pages: 2185 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 13 |
Transformer-Based Abstractive Summarization for Reddit and Twitter: Single Posts vs. Comment Pools in Three Languages
|
|
|
|
In: Future Internet; Volume 14; Issue 3; Pages: 69 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 14 |
Correcting Diacritics and Typos with a ByT5 Transformer Model
|
|
|
|
In: Applied Sciences; Volume 12; Issue 5; Pages: 2636 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 15 |
Language Competition and Language Shift in Friuli-Venezia Giulia: Projection and Trajectory for the Number of Friulian Speakers to 2050
|
|
|
|
In: Sustainability; Volume 14; Issue 6; Pages: 3319 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 16 |
An Information Theoretic Approach to Symbolic Learning in Synthetic Languages
|
|
|
|
In: Entropy; Volume 24; Issue 2; Pages: 259 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 17 |
Comparison of Text Mining Models for Food and Dietary Constituent Named-Entity Recognition
|
|
|
|
In: Machine Learning and Knowledge Extraction; Volume 4; Issue 1; Pages: 254-275 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 19 |
Language and vision in conceptual processing: Multilevel analysis and statistical power ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 20 |
Exploring the Representations of Individual Entities in the Brain Combining EEG and Distributional Semantics.
|
|
|
|
BASE
|
|
Show details
|
|
|
|
|
