Joint Multimodal Contrastive Learning for Robust Spoken Term Detection and Keyword Spotting

• URL: http://arxiv.org/abs/2512.14115v1
• Date: Tue, 16 Dec 2025 05:58:25 GMT
• Title: Joint Multimodal Contrastive Learning for Robust Spoken Term Detection and Keyword Spotting
• Authors: Ramesh Gundluru, Shubham Gupta, Sri Rama Murty K,
• Abstract summary: We propose a joint multimodal contrastive learning framework that unifies acoustic and cross-modal supervision in a shared embedding space.<n>Our approach simultaneously optimize: (i) audio-text contrastive learning, inspired by the CLAP loss, to align audio and text representations and (ii) audio-audio contrastive learning, via Deep Word Discrimination (DWD) loss, to enhance intra-class compactness and inter-class separation.<n>The proposed method outperforms existing AWE baselines on word discrimination task while flexibly supporting both STD and KWS.
• Score: 13.48022380380599
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Acoustic Word Embeddings (AWEs) improve the efficiency of speech retrieval tasks such as Spoken Term Detection (STD) and Keyword Spotting (KWS). However, existing approaches suffer from limitations, including unimodal supervision, disjoint optimization of audio-audio and audio-text alignment, and the need for task-specific models. To address these shortcomings, we propose a joint multimodal contrastive learning framework that unifies both acoustic and cross-modal supervision in a shared embedding space. Our approach simultaneously optimizes: (i) audio-text contrastive learning, inspired by the CLAP loss, to align audio and text representations and (ii) audio-audio contrastive learning, via Deep Word Discrimination (DWD) loss, to enhance intra-class compactness and inter-class separation. The proposed method outperforms existing AWE baselines on word discrimination task while flexibly supporting both STD and KWS. To our knowledge, this is the first comprehensive approach of its kind.

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)
• CORD: Bridging the Audio-Text Reasoning Gap via Weighted On-policy Cross-modal Distillation [32.72685791637924]
  We propose CORD, a unified alignment framework that performs online cross-modal self-distillation.<n>Specifically, it aligns audio-conditioned reasoning with its text-conditioned counterpart within a unified model.<n> Empirical results across multiple benchmarks demonstrate that CORD consistently enhances audio-conditioned reasoning.
  arXiv  Detail & Related papers  (2026-01-23T08:31:24Z)
• Towards Inclusive Communication: A Unified Framework for Generating Spoken Language from Sign, Lip, and Audio [52.859261069569165]
  We propose the first unified framework capable of handling diverse combinations of sign language, lip movements, and audio for spoken-language text generation.<n>We focus on three main objectives: (i) designing a unified, modality-agnostic architecture capable of effectively processing heterogeneous inputs; (ii) exploring the underexamined synergy among modalities, particularly the role of lip movements as non-manual cues in sign language comprehension; and (iii) achieving performance on par with or better than state-of-the-art models specialized for individual tasks.
  arXiv  Detail & Related papers  (2025-08-28T06:51:42Z)
• Implicit Counterfactual Learning for Audio-Visual Segmentation [50.69377287012591]
  We propose the implicit counterfactual framework (ICF) to achieve unbiased cross-modal understanding.<n>Due to the lack of semantics, heterogeneous representations may lead to erroneous matches.<n>We introduce the multi-granularity implicit text (MIT) involving video-, segment- and frame-level as the bridge to establish the modality-shared space.
  arXiv  Detail & Related papers  (2025-07-28T11:46:35Z)
• Towards Robust Overlapping Speech Detection: A Speaker-Aware Progressive Approach Using WavLM [53.17360668423001]
  Overlapping Speech Detection (OSD) aims to identify regions where multiple speakers overlap in a conversation.<n>This work proposes a speaker-aware progressive OSD model that leverages a progressive training strategy to enhance the correlation between subtasks.<n> Experimental results show that the proposed method achieves state-of-the-art performance, with an F1 score of 82.76% on the AMI test set.
  arXiv  Detail & Related papers  (2025-05-29T07:47:48Z)
• Adversarial Deep Metric Learning for Cross-Modal Audio-Text Alignment in Open-Vocabulary Keyword Spotting [8.401528952094413]
  For text enrollment-based open-vocabulary keyword spotting (KWS), acoustic and text embeddings are typically compared at either the phoneme or utterance level.<n>We optimize acoustic and text encoders using deep metric learning (DML), enabling direct comparison of multi-modal embeddings in a shared embedding space.<n>We propose Modality Adversarial Learning (MAL), which reduces the domain gap in heterogeneous modality representations.
  arXiv  Detail & Related papers  (2025-05-22T14:49:46Z)
• Integrating Audio, Visual, and Semantic Information for Enhanced Multimodal Speaker Diarization [25.213694510527436]
  Most existing speaker diarization systems rely exclusively on unimodal acoustic information. We propose a novel multimodal approach that jointly utilizes audio, visual, and semantic cues to enhance speaker diarization. Our approach consistently outperforms state-of-the-art speaker diarization methods.
  arXiv  Detail & Related papers  (2024-08-22T03:34:03Z)
• Learning Speech Representation From Contrastive Token-Acoustic Pretraining [57.08426714676043]
  We propose "Contrastive Token-Acoustic Pretraining (CTAP)", which uses two encoders to bring phoneme and speech into a joint multimodal space. The proposed CTAP model is trained on 210k speech and phoneme pairs, achieving minimally-supervised TTS, VC, and ASR.
  arXiv  Detail & Related papers  (2023-09-01T12:35:43Z)
• End-to-End Active Speaker Detection [58.7097258722291]
  We propose an end-to-end training network where feature learning and contextual predictions are jointly learned. We also introduce intertemporal graph neural network (iGNN) blocks, which split the message passing according to the main sources of context in the ASD problem. Experiments show that the aggregated features from the iGNN blocks are more suitable for ASD, resulting in state-of-the art performance.
  arXiv  Detail & Related papers  (2022-03-27T08:55:28Z)

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.