Audio-Textual Diffusion Model for Generating Ultrasound Tongue Imaging Data

Core Concepts

The author proposes an audio-textual diffusion model to generate high-quality UTI data with clear tongue contour, crucial for linguistic analysis and clinical assessment.

Abstract

An audio-textual diffusion model is introduced to convert speech signals into ultrasound tongue imaging (UTI) data. By integrating acoustic and textual information, the proposed model generates high-quality UTI data with a clear tongue contour. Experimental results demonstrate significant improvements over traditional methods in terms of quality metrics such as RMSE, LPIPS, PSNR, and FID. The study addresses the limitations of existing Acoustic-to-Articulatory Inversion (AAI) methods by leveraging personalized acoustic information and universal textual inputs. The diffusion model captures temporal dependencies across consecutive pronunciations to enhance the quality of generated UTI data. The fusion of acoustic and textual embeddings through a cross-attention mechanism improves the coherence and naturalness of the generated UTI data. By applying the diffusion model to convert audio into UTI data, the study showcases advancements in speech technology applications like automatic speech recognition, speech therapy, and speech assessment. The proposed system outperforms DNN-based AAI systems in various evaluation metrics, highlighting its potential for clinical assessments related to tongue function.

Stats

Experimental results showed that the proposed diffusion model could generate high-quality UTI data. A LPIPS improvement of 67.95% relative was achieved by the diffusion system. FID decreased from 256.80 to 22.02 with the proposed diffusion AAI system. The training set consists of 40 speakers with a total dataset duration of 6.85 hours. The ASR model achieved a character error rate (CER) of 0.02% on the Mandarin speech-ultrasound dataset.

Quotes

"The proposed diffusion AAI system consistently outperformed DNN-based AAI baselines in all metrics." "The introduction of additional textual information significantly enhanced the quality of generated UTI data." "The fusion of acoustic and textual embeddings improved coherence and naturalness in generated UTI data."

Key Insights Distilled From

An Audio-textual Diffusion Model For Converting Speech Signals Into Ultrasound Tongue Imaging Data

by Yudong Yang,... at arxiv.org 03-12-2024

https://arxiv.org/pdf/2403.05820.pdf

Deeper Inquiries

How can the integration of additional textual information impact other areas beyond speech technology

The integration of additional textual information in the context of converting speech signals into ultrasound tongue imaging (UTI) data can have far-reaching impacts beyond just speech technology. By incorporating text inputs that contain universal information related to tongue movements, the model's ability to generate clear and coherent UTI data is significantly enhanced. This approach not only improves the quality of generated UTI data for linguistic analysis and clinical assessment but also opens up possibilities in various fields. One area where this integration could be beneficial is in medical research and diagnostics. Clear visualization of tongue contours through high-quality UTI data can aid in diagnosing speech disorders, tracking progress in speech therapy, or even detecting early signs of certain health conditions affecting oral motor function. The detailed articulatory information derived from audio-textual diffusion models could provide valuable insights for healthcare professionals working with patients who have speech-related issues. Moreover, advancements in generating precise UTI data using diffusion models may find applications in biometric authentication systems. The unique patterns captured by these models based on individualized acoustic characteristics and universal textual representations could potentially be leveraged for secure voice-based identification methods. In educational settings, such technologies could enhance language learning tools by providing real-time feedback on pronunciation accuracy based on detailed articulatory movements inferred from speech signals. Students aiming to improve their language skills could benefit from personalized feedback generated by integrating audio-textual information into language learning platforms. Overall, the integration of additional textual information has the potential to revolutionize various domains beyond speech technology by enabling more accurate and detailed analyses based on multimodal input sources.

What are potential counterarguments against using diffusion models for generating UTI data

While diffusion models offer significant advantages for generating high-quality UTI data with clear tongue contour, there are potential counterarguments against their use: Complexity: Diffusion models often involve intricate training processes and require substantial computational resources compared to traditional deep neural network approaches. Critics might argue that the complexity associated with implementing diffusion models for generating UTI data could hinder widespread adoption due to resource constraints or technical expertise requirements. Interpretability: Some researchers may raise concerns about the interpretability of results obtained from diffusion models when applied to complex tasks like converting audio into visual representations such as UTI data. Understanding how these models arrive at specific outputs or making adjustments based on domain-specific knowledge might pose challenges that limit their practical utility. Data Efficiency: While diffusion models excel at capturing fine details and nuances within datasets, skeptics might question their efficiency when dealing with limited training samples—such as small-scale speech-ultrasound parallel datasets commonly encountered in real-world applications. 4 .Generalization: Another counterargument against using diffusion models for generating UTI data lies in their ability to generalize across diverse speakers or languages effectively. Addressing these counterarguments would require further research focusing on simplifying model architectures without compromising performance, enhancing interpretability through explainable AI techniques tailored to diffusion modeling specifics, optimizing training strategies for efficient utilization of limited datasets while ensuring robust generalization capabilities across different contexts.

How might advancements in image synthesis through diffusion models influence future applications unrelated to speech technology

Advancements in image synthesis facilitated by diffusion models hold immense promise beyond applications directly related to speech technology: 1 .Artificial Intelligence & Robotics: The sophisticated image synthesis capabilities enabled by diffusion models can revolutionize AI-driven systems' perceptual abilities—for instance, enhancing computer vision algorithms used in autonomous vehicles or robotics platforms. 2 .Healthcare Imaging: In medical imaging fields like radiology or pathology, diffusion-based image generation techniques can contribute to creating synthetic images useful for diagnostic purposes, training healthcare professionals without relying solely on scarce real patient scans 3 .Entertainment Industry: Film production studios and game developers stand poised to leverage advanced image synthesis methods powered by diffusions—augmenting special effects creation, virtual world building,and character animation pipelines 4 .Fashion & Design: Diffusion-generated images have great potentialin fashion design prototyping,catalog creation,and virtual try-on experiencesfor online shoppers By pushing boundariesin realistic image synthesis,diffusionmodels pave wayfor innovative solutionsacross diverse sectorsbeyondspeechtechnology,redefininghow we interactwith visualdataand openingnew avenuesof creativityand application