Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models

• URL: http://arxiv.org/abs/2505.23378v2
• Date: Mon, 02 Jun 2025 10:11:58 GMT
• Title: Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models
• Authors: Roseline Polle, Agnes Norbury, Alexandra Livia Georgescu, Nicholas Cummins, Stefano Goria,
• Abstract summary: We reformulate this task as a meta-learning problem and explore three approaches of increasing complexity.<n>Using pre-trained speech embeddings, we evaluate these methods on a large longitudinal dataset of shift workers.<n>Our results demonstrate that all meta-learning approaches tested outperformed both cross-sectional and conventional mixed-effects models.
• Score: 45.81793540247952
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Speaker-dependent modelling can substantially improve performance in speech-based health monitoring applications. While mixed-effect models are commonly used for such speaker adaptation, they require computationally expensive retraining for each new observation, making them impractical in a production environment. We reformulate this task as a meta-learning problem and explore three approaches of increasing complexity: ensemble-based distance models, prototypical networks, and transformer-based sequence models. Using pre-trained speech embeddings, we evaluate these methods on a large longitudinal dataset of shift workers (N=1,185, 10,286 recordings), predicting time since sleep from speech as a function of fatigue, a symptom commonly associated with ill-health. Our results demonstrate that all meta-learning approaches tested outperformed both cross-sectional and conventional mixed-effects models, with a transformer-based method achieving the strongest performance.

Related papers

• Comparing Self-Supervised Learning Models Pre-Trained on Human Speech and Animal Vocalizations for Bioacoustics Processing [19.205671029694074]
  Self-supervised learning (SSL) foundation models have emerged as powerful, domain-agnostic, general-purpose feature extractors.<n>This paper investigates whether SSL models pre-trained directly on animal vocalizations offer a significant advantage over those pre-trained on speech.
  arXiv  Detail & Related papers  (2025-01-10T14:18:21Z)
• Adaptive Training Meets Progressive Scaling: Elevating Efficiency in Diffusion Models [52.1809084559048]
  We propose a novel two-stage divide-and-conquer training strategy termed TDC Training.<n>It groups timesteps based on task similarity and difficulty, assigning highly customized denoising models to each group, thereby enhancing the performance of diffusion models.<n>While two-stage training avoids the need to train each model separately, the total training cost is even lower than training a single unified denoising model.
  arXiv  Detail & Related papers  (2023-12-20T03:32:58Z)
• Generative Pre-training for Speech with Flow Matching [81.59952572752248]
  We pre-trained a generative model, named SpeechFlow, on 60k hours of untranscribed speech with Flow Matching and masked conditions. Experiment results show the pre-trained generative model can be fine-tuned with task-specific data to match or surpass existing expert models on speech enhancement, separation, and synthesis.
  arXiv  Detail & Related papers  (2023-10-25T03:40:50Z)
• High-Fidelity Speech Synthesis with Minimal Supervision: All Using Diffusion Models [56.00939852727501]
  Minimally-supervised speech synthesis decouples TTS by combining two types of discrete speech representations. Non-autoregressive framework enhances controllability, and duration diffusion model enables diversified prosodic expression.
  arXiv  Detail & Related papers  (2023-09-27T09:27:03Z)
• End-to-End Speech Recognition and Disfluency Removal with Acoustic Language Model Pretraining [0.0]
  We revisit the performance comparison between two-stage and end-to-end model. We find that audio based language models pretrained using weak self-supervised objectives match or exceed the performance of similarly trained two-stage models.
  arXiv  Detail & Related papers  (2023-09-08T17:12:14Z)
• Minimally-Supervised Speech Synthesis with Conditional Diffusion Model and Language Model: A Comparative Study of Semantic Coding [57.42429912884543]
  We propose Diff-LM-Speech, Tetra-Diff-Speech and Tri-Diff-Speech to solve high dimensionality and waveform distortion problems. We also introduce a prompt encoder structure based on a variational autoencoder and a prosody bottleneck to improve prompt representation ability. Experimental results show that our proposed methods outperform baseline methods.
  arXiv  Detail & Related papers  (2023-07-28T11:20:23Z)
• DDKtor: Automatic Diadochokinetic Speech Analysis [13.68342426889044]
  This paper presents two deep neural network models that automatically segment consonants and vowels from unannotated, untranscribed speech. Results on a young healthy individuals dataset show that our LSTM model outperforms the current state-of-the-art systems. The LSTM model also presents comparable results to trained human annotators when evaluated on unseen older individuals with Parkinson's Disease dataset.
  arXiv  Detail & Related papers  (2022-06-29T13:34:03Z)
• Self-Normalized Importance Sampling for Neural Language Modeling [97.96857871187052]
  In this work, we propose self-normalized importance sampling. Compared to our previous work, the criteria considered in this work are self-normalized and there is no need to further conduct a correction step. We show that our proposed self-normalized importance sampling is competitive in both research-oriented and production-oriented automatic speech recognition tasks.
  arXiv  Detail & Related papers  (2021-11-11T16:57:53Z)
• Avoiding Inference Heuristics in Few-shot Prompt-based Finetuning [57.4036085386653]
  We show that prompt-based models for sentence pair classification tasks still suffer from a common pitfall of adopting inferences based on lexical overlap. We then show that adding a regularization that preserves pretraining weights is effective in mitigating this destructive tendency of few-shot finetuning.
  arXiv  Detail & Related papers  (2021-09-09T10:10:29Z)
• Self-Supervised Learning from Contrastive Mixtures for Personalized Speech Enhancement [19.645016575334786]
  This work explores how self-supervised learning can be universally used to discover speaker-specific features. We develop a simple contrastive learning procedure which treats the abundant noisy data as makeshift training targets.
  arXiv  Detail & Related papers  (2020-11-06T15:21:00Z)
• A Spectral Energy Distance for Parallel Speech Synthesis [29.14723501889278]
  Speech synthesis is an important practical generative modeling problem. We propose a new learning method that allows us to train highly parallel models of speech.
  arXiv  Detail & Related papers  (2020-08-03T19:56:04Z)

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.