CA-SSLR: Condition-Aware Self-Supervised Learning Representation for Generalized Speech Processing

• URL: http://arxiv.org/abs/2412.04425v1
• Date: Thu, 05 Dec 2024 18:51:10 GMT
• Title: CA-SSLR: Condition-Aware Self-Supervised Learning Representation for Generalized Speech Processing
• Authors: Yen-Ju Lu, Jing Liu, Thomas Thebaud, Laureano Moro-Velazquez, Ariya Rastrow, Najim Dehak, Jesus Villalba,
• Abstract summary: We introduce Condition-Aware Self-Supervised Learning Representation (CA-SSLR) CA-S SLR improves the model's capabilities and demonstrates its generality on unseen tasks. Experiments show that CA-S SLR reduces the number of trainable parameters, mitigates overfitting, and excels in under-resourced and unseen tasks.
• Score: 27.828675312638296
• License:
• Abstract: We introduce Condition-Aware Self-Supervised Learning Representation (CA-SSLR), a generalist conditioning model broadly applicable to various speech-processing tasks. Compared to standard fine-tuning methods that optimize for downstream models, CA-SSLR integrates language and speaker embeddings from earlier layers, making the SSL model aware of the current language and speaker context. This approach reduces the reliance on input audio features while preserving the integrity of the base SSLR. CA-SSLR improves the model's capabilities and demonstrates its generality on unseen tasks with minimal task-specific tuning. Our method employs linear modulation to dynamically adjust internal representations, enabling fine-grained adaptability without significantly altering the original model behavior. Experiments show that CA-SSLR reduces the number of trainable parameters, mitigates overfitting, and excels in under-resourced and unseen tasks. Specifically, CA-SSLR achieves a 10% relative reduction in LID errors, a 37% improvement in ASR CER on the ML-SUPERB benchmark, and a 27% decrease in SV EER on VoxCeleb-1, demonstrating its effectiveness.

Related papers

• Improving Transducer-Based Spoken Language Understanding with Self-Conditioned CTC and Knowledge Transfer [11.362681035467121]
  We propose to improve end-to-end (E2E) spoken language understand (SLU) in an RNN transducer model (RNN-T) Our proposed model is akin to an E2E differentiable cascaded model which performs ASR and SLU sequentially.
  arXiv  Detail & Related papers  (2025-01-03T18:19:12Z)
• Training Strategies for Isolated Sign Language Recognition [72.27323884094953]
  This paper introduces a comprehensive model training pipeline for Isolated Sign Language Recognition. The constructed pipeline incorporates carefully selected image and video augmentations to tackle the challenges of low data quality and varying sign speeds. We achieve a state-of-the-art result on the WLASL and Slovo benchmarks with 1.63% and 14.12% improvements compared to the previous best solution.
  arXiv  Detail & Related papers  (2024-12-16T08:37:58Z)
• How to Learn a New Language? An Efficient Solution for Self-Supervised Learning Models Unseen Languages Adaption in Low-Resource Scenario [72.02391485962127]
  Speech Self-Supervised Learning (SSL) models achieve impressive performance on Automatic Speech Recognition (ASR) In low-resource language ASR, they encounter the domain mismatch problem between pre-trained and low-resource languages. We extend a conventional efficient fine-tuning scheme based on the adapter to handle these issues.
  arXiv  Detail & Related papers  (2024-11-27T10:51:00Z)
• Selective Self-Rehearsal: A Fine-Tuning Approach to Improve Generalization in Large Language Models [19.752712857873043]
  This paper introduces Selective Self-Rehearsal (SSR), a fine-tuning approach that achieves performance comparable to the standard supervised fine-tuning (SFT) By utilizing the model's correct responses, SSR reduces model specialization during the fine-tuning stage. The effectiveness of SSR is demonstrated through experiments on the task of identifying unanswerable queries across various datasets.
  arXiv  Detail & Related papers  (2024-09-07T10:21:03Z)
• Enhancing Robustness of Vision-Language Models through Orthogonality Learning and Self-Regularization [77.62516752323207]
  We introduce an orthogonal fine-tuning method for efficiently fine-tuning pretrained weights and enabling enhanced robustness and generalization. A self-regularization strategy is further exploited to maintain the stability in terms of zero-shot generalization of VLMs, dubbed OrthSR. For the first time, we revisit the CLIP and CoOp with our method to effectively improve the model on few-shot image classficiation scenario.
  arXiv  Detail & Related papers  (2024-07-11T10:35:53Z)
• ML-SUPERB 2.0: Benchmarking Multilingual Speech Models Across Modeling Constraints, Languages, and Datasets [106.7760874400261]
  This paper presents ML-SUPERB2.0, which is a new benchmark for evaluating pre-trained SSL and supervised speech models. We find performance improvements over the setup of ML-SUPERB, but performance depends on the downstream model design. Also, we find large performance differences between languages and datasets, suggesting the need for more targeted approaches.
  arXiv  Detail & Related papers  (2024-06-12T21:01:26Z)
• Towards Supervised Performance on Speaker Verification with Self-Supervised Learning by Leveraging Large-Scale ASR Models [0.0]
  Speech representations from large-scale ASR models contain valuable speaker information. We propose a framework to learn speaker representations in an SSL context by fine-tuning a pre-trained WavLM with a supervised loss. Our method achieves 0.99% EER on VoxCeleb1-O, establishing the new state-of-the-art on self-supervised SV.
  arXiv  Detail & Related papers  (2024-06-04T12:58:19Z)
• Attribute-Modulated Generative Meta Learning for Zero-Shot Classification [52.64680991682722]
  We present the Attribute-Modulated generAtive meta-model for Zero-shot learning (AMAZ) Our model consists of an attribute-aware modulation network and an attribute-augmented generative network. Our empirical evaluations show that AMAZ improves state-of-the-art methods by 3.8% and 5.1% in ZSL and generalized ZSL settings, respectively.
  arXiv  Detail & Related papers  (2021-04-22T04:16:43Z)
• Joint Contextual Modeling for ASR Correction and Language Understanding [60.230013453699975]
  We propose multi-task neural approaches to perform contextual language correction on ASR outputs jointly with language understanding (LU) We show that the error rates of off the shelf ASR and following LU systems can be reduced significantly by 14% relative with joint models trained using small amounts of in-domain data.
  arXiv  Detail & Related papers  (2020-01-28T22:09:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.