MuFFIN: Multifaceted Pronunciation Feedback Model with Interactive Hierarchical Neural Modeling

• URL: http://arxiv.org/abs/2510.04956v2
• Date: Tue, 07 Oct 2025 14:43:24 GMT
• Title: MuFFIN: Multifaceted Pronunciation Feedback Model with Interactive Hierarchical Neural Modeling
• Authors: Bi-Cheng Yan, Ming-Kang Tsai, Berlin Chen,
• Abstract summary: We introduce MuFFIN, a Multi-Faceted pronunciation Feedback model with an Interactive hierarchical Neural architecture.<n>To better capture the nuanced distinctions between phonemes in the feature space, a novel phoneme-contrastive ordinal regularization mechanism is put forward.<n>We design a simple yet effective training objective, which is specifically tailored to perturb the outputs of a phoneme with the phoneme-specific variations.
• Score: 14.953695326450001
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Computer-assisted pronunciation training (CAPT) manages to facilitate second-language (L2) learners to practice pronunciation skills by offering timely and instructive feedback. To examine pronunciation proficiency from multiple facets, existing methods for CAPT broadly fall into two categories: mispronunciation detection and diagnosis (MDD) as well as automatic pronunciation assessment (APA). The former aims to pinpoint phonetic pronunciation errors and provide diagnostic feedback, while the latter seeks instead to quantify pronunciation proficiency pertaining to various aspects. Despite the natural complementarity between MDD and APA, researchers and practitioners, however, often treat them as independent tasks with disparate modeling paradigms. In light of this, we in this paper first introduce MuFFIN, a Multi-Faceted pronunciation Feedback model with an Interactive hierarchical Neural architecture, to jointly address the tasks of MDD and APA. To better capture the nuanced distinctions between phonemes in the feature space, a novel phoneme-contrastive ordinal regularization mechanism is then put forward to optimize the proposed model to generate more phoneme-discriminative features while factoring in the ordinality of the aspect scores. In addition, to address the intricate data imbalance problem in MDD, we design a simple yet effective training objective, which is specifically tailored to perturb the outputs of a phoneme classifier with the phoneme-specific variations, so as to better render the distribution of predicted phonemes meanwhile considering their mispronunciation characteristics. A series of experiments conducted on the Speechocean762 benchmark dataset demonstrates the efficacy of our method in relation to several cutting-edge baselines, showing state-of-the-art performance on both the APA and MDD tasks.

Related papers

• Optimizing Conversational Quality in Spoken Dialogue Systems with Reinforcement Learning from AI Feedback [82.70507055599093]
  We present the first systematic study of preference learning for improving SDS quality in both multi-turn Chain-of-Thought and blockwise duplex models.<n>Experiments show that single-reward RLAIF selectively improves its targeted metric, while joint multi-reward training yields consistent gains across semantic quality and audio naturalness.
  arXiv  Detail & Related papers  (2026-01-27T00:55:14Z)
• Multi-granularity Interactive Attention Framework for Residual Hierarchical Pronunciation Assessment [18.97451964522765]
  We propose a novel residual hierarchical interactive method, HIA, that enables bidirectional modeling across granularities.<n>We also propose a residual hierarchical structure to alleviate the feature forgetting problem when modeling acoustic hierarchies.<n>Our model is comprehensively ahead of the existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2026-01-05T02:43:04Z)
• Multi-task Pretraining for Enhancing Interpretable L2 Pronunciation Assessment [21.12585023191302]
  Automatic pronunciation assessment (APA) analyzes second-language (L2) learners' speech by providing fine-grained pronunciation feedback.<n>Most existing efforts on APA typically adopt segmental-level features as inputs and predict pronunciation scores at different granularities.<n>We introduce multi-task pretraining (MTP) for APA, a simple yet effective strategy that attempts to capture long-term temporal pronunciation cues.
  arXiv  Detail & Related papers  (2025-09-21T02:04:52Z)
• JCAPT: A Joint Modeling Approach for CAPT [4.1088673993841685]
  This study is the first to combine phonological attribution, SSM-based modeling, and prompting in CAPT.<n>A series of experiments conducted on the speechocean762 benchmark demonstrate that our model consistently outperforms prior methods.
  arXiv  Detail & Related papers  (2025-06-24T05:12:32Z)
• Towards Efficient and Multifaceted Computer-assisted Pronunciation Training Leveraging Hierarchical Selective State Space Model and Decoupled Cross-entropy Loss [5.101375571703936]
  HMamba is a novel CAPT approach that seamlessly integrates APA and MDD tasks in parallel.<n>A comprehensive set of empirical results on the speechocean762 benchmark dataset demonstrates the effectiveness of our approach on APA.<n>Our proposed approach also yields a considerable improvement in MDD performance over a strong baseline, achieving an F1-score of 63.85%.
  arXiv  Detail & Related papers  (2025-02-11T14:17:29Z)
• Preserving Phonemic Distinctions for Ordinal Regression: A Novel Loss Function for Automatic Pronunciation Assessment [10.844822448167937]
  We propose a phonemic contrast ordinal (PCO) loss for training regression-based APA models. Specifically, we introduce a phoneme-distinct regularizer into the MSE loss, which encourages feature representations of different phoneme categories to be far apart. An extensive set of experiments carried out on the speechocean762 benchmark dataset suggest the feasibility and effectiveness of our model.
  arXiv  Detail & Related papers  (2023-10-03T07:05:37Z)
• 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)
• Towards Language Modelling in the Speech Domain Using Sub-word Linguistic Units [56.52704348773307]
  We propose a novel LSTM-based generative speech LM based on linguistic units including syllables and phonemes. With a limited dataset, orders of magnitude smaller than that required by contemporary generative models, our model closely approximates babbling speech. We show the effect of training with auxiliary text LMs, multitask learning objectives, and auxiliary articulatory features.
  arXiv  Detail & Related papers  (2021-10-31T22:48:30Z)
• SPLAT: Speech-Language Joint Pre-Training for Spoken Language Understanding [61.02342238771685]
  Spoken language understanding requires a model to analyze input acoustic signal to understand its linguistic content and make predictions. Various pre-training methods have been proposed to learn rich representations from large-scale unannotated speech and text. We propose a novel semi-supervised learning framework, SPLAT, to jointly pre-train the speech and language modules.
  arXiv  Detail & Related papers  (2020-10-05T19:29:49Z)
• Filling the Gap of Utterance-aware and Speaker-aware Representation for Multi-turn Dialogue [76.88174667929665]
  A multi-turn dialogue is composed of multiple utterances from two or more different speaker roles. In the existing retrieval-based multi-turn dialogue modeling, the pre-trained language models (PrLMs) as encoder represent the dialogues coarsely. We propose a novel model to fill such a gap by modeling the effective utterance-aware and speaker-aware representations entailed in a dialogue history.
  arXiv  Detail & Related papers  (2020-09-14T15:07:19Z)
• Any-to-Many Voice Conversion with Location-Relative Sequence-to-Sequence Modeling [61.351967629600594]
  This paper proposes an any-to-many location-relative, sequence-to-sequence (seq2seq), non-parallel voice conversion approach. In this approach, we combine a bottle-neck feature extractor (BNE) with a seq2seq synthesis module. Objective and subjective evaluations show that the proposed any-to-many approach has superior voice conversion performance in terms of both naturalness and speaker similarity.
  arXiv  Detail & Related papers  (2020-09-06T13:01:06Z)

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.