Medical Speech Symptoms Classification via Disentangled Representation

Core Concepts

The author proposes a model, DRSC, that disentangles intent information from text and Mel-spectrogram domains to improve medical speech symptoms classification accuracy.

Abstract

The paper introduces DRSC, a model that extracts intent information from both text and Mel-spectrogram domains for classification. It addresses the limitations of existing methods by considering both textual and acoustic features in medical speech. The proposed model achieves an average accuracy rate of 95% in detecting 25 different medical symptoms. By disentangling intent representations and content features, DRSC enhances the diagnosis of symptoms using multi-modal data. The study highlights the importance of combining textual and acoustic information for accurate symptom classification.

Stats

Our model obtains an average accuracy rate of 95% in detecting 25 different medical symptoms. The dataset contains 6,661 audio segments with varying lengths. The proposed model outperforms existing methods in classification accuracy. ASR model produces a word error rate of 26% in transcribing speech text.

Quotes

"No early deep learning works consider the acoustic feature of patients' speeches." "DRSC achieves satisfactory performance and robustness even with inaccurate transcriptions." "Our proposed method disentangles intent representations from text and Mel-spectrogram domains."

Key Insights Distilled From

Medical Speech Symptoms Classification via Disentangled Representation

by Jianzong Wan... at arxiv.org 03-11-2024

https://arxiv.org/pdf/2403.05000.pdf

Deeper Inquiries

How can the integration of acoustic features enhance the accuracy of symptom classification

The integration of acoustic features can significantly enhance the accuracy of symptom classification in medical speech diagnosis models. Acoustic features such as pitch, rhythm, and energy carry crucial information related to symptoms that may not be fully captured by textual data alone. For instance, a patient suffering from a sore throat may exhibit distinct acoustic patterns in their speech compared to someone with a different condition. By incorporating these acoustic features into the classification model, it becomes possible to capture subtle nuances in speech that are indicative of specific symptoms. This multi-modal approach allows for a more comprehensive analysis of the patient's speech signals, leading to improved accuracy in identifying and classifying medical conditions based on symptomatic presentations.

What are the implications of using inaccurate transcriptions on medical speech diagnosis models

Using inaccurate transcriptions in medical speech diagnosis models can have significant implications on the overall performance and reliability of the system. Inaccurate transcriptions introduce errors and inconsistencies into the input data fed into the model, potentially leading to misinterpretations or incorrect classifications of symptoms. These errors can impact various stages of the diagnostic process, including feature extraction, intent recognition, and ultimately symptom classification. The presence of inaccuracies may result in misaligned representations between text and acoustic domains, affecting the disentanglement process crucial for extracting relevant intent information from both modalities accurately. Furthermore, inaccurate transcriptions can hinder training effectiveness by introducing noise and ambiguity into the dataset used for model development. This could lead to suboptimal learning outcomes and reduced generalization capabilities when faced with real-world scenarios where accurate transcription is essential for precise diagnosis.

How can disentangled representations benefit other areas beyond medical speech classification

Disentangled representations derived from multi-modal data have broader implications beyond medical speech classification that extend to various other fields and applications: Emotion Recognition: Disentangled representations can aid in emotion recognition tasks by separating affective content from contextual information present in audio-visual data streams. Anomaly Detection: In anomaly detection systems across industries like cybersecurity or predictive maintenance, disentangled representations help isolate abnormal patterns or behaviors within complex datasets. Natural Language Processing (NLP): Disentangling semantic meaning from syntactic structures enables more nuanced understanding during language processing tasks such as sentiment analysis or machine translation. Image Analysis: Applied in image processing tasks like style transfer or object recognition where disentangled factors like texture vs shape play critical roles. 5Human-Computer Interaction (HCI): Enhancing user experience through personalized interfaces leveraging disentangled user preferences extracted from interaction logs. By leveraging disentangled representations across diverse domains beyond medical speech classification enhances interpretability, robustness,and efficiency while enabling advanced AI applications requiring nuanced understanding of complex multimodal data sources..