Robust Speech and Natural Language Processing Models for Depression Screening

• URL: http://arxiv.org/abs/2412.19072v1
• Date: Thu, 26 Dec 2024 06:05:52 GMT
• Title: Robust Speech and Natural Language Processing Models for Depression Screening
• Authors: Y. Lu, A. Harati, T. Rutowski, R. Oliveira, P. Chlebek, E. Shriberg,
• Abstract summary: Depression is a global health concern with a critical need for increased patient screening. We have described two deep learning models developed for this purpose. One model is based on acoustics; the other is based on natural language processing.
• Score: 0.0
• License:
• Abstract: Depression is a global health concern with a critical need for increased patient screening. Speech technology offers advantages for remote screening but must perform robustly across patients. We have described two deep learning models developed for this purpose. One model is based on acoustics; the other is based on natural language processing. Both models employ transfer learning. Data from a depression-labeled corpus in which 11,000 unique users interacted with a human-machine application using conversational speech is used. Results on binary depression classification have shown that both models perform at or above AUC=0.80 on unseen data with no speaker overlap. Performance is further analyzed as a function of test subset characteristics, finding that the models are generally robust over speaker and session variables. We conclude that models based on these approaches offer promise for generalized automated depression screening.

Related papers

• AIR-Bench: Benchmarking Large Audio-Language Models via Generative Comprehension [95.8442896569132]
  We introduce AIR-Bench, the first benchmark to evaluate the ability of Large Audio-Language Models (LALMs) to understand various types of audio signals and interact with humans in the textual format. Results demonstrate a high level of consistency between GPT-4-based evaluation and human evaluation.
  arXiv  Detail & Related papers  (2024-02-12T15:41:22Z)
• Detecting Speech Abnormalities with a Perceiver-based Sequence Classifier that Leverages a Universal Speech Model [4.503292461488901]
  We propose a Perceiver-based sequence to detect abnormalities in speech reflective of several neurological disorders. We combine this sequence with a Universal Speech Model (USM) that is trained (unsupervised) on 12 million hours of diverse audio recordings. Our model outperforms standard transformer (80.9%) and perceiver (81.8%) models and achieves an average accuracy of 83.1%.
  arXiv  Detail & Related papers  (2023-10-16T21:07:12Z)
• Exploring Speech Recognition, Translation, and Understanding with Discrete Speech Units: A Comparative Study [68.88536866933038]
  Speech signals, typically sampled at rates in the tens of thousands per second, contain redundancies. Recent investigations proposed the use of discrete speech units derived from self-supervised learning representations. Applying various methods, such as de-duplication and subword modeling, can further compress the speech sequence length.
  arXiv  Detail & Related papers  (2023-09-27T17:21:13Z)
• Analysing the Impact of Audio Quality on the Use of Naturalistic Long-Form Recordings for Infant-Directed Speech Research [62.997667081978825]
  Modelling of early language acquisition aims to understand how infants bootstrap their language skills. Recent developments have enabled the use of more naturalistic training data for computational models. It is currently unclear how the sound quality could affect analyses and modelling experiments conducted on such data.
  arXiv  Detail & Related papers  (2023-05-03T08:25:37Z)
• NaturalSpeech 2: Latent Diffusion Models are Natural and Zero-Shot Speech and Singing Synthesizers [90.83782600932567]
  We develop NaturalSpeech 2, a TTS system that leverages a neural audio predictor with residual vectorizers to get the quantized latent vectors. We scale NaturalSpeech 2 to large-scale datasets with 44K hours of speech and singing data and evaluate its voice quality on unseen speakers. NaturalSpeech 2 outperforms previous TTS systems by a large margin in terms of prosody/timbre similarity, synthesis, and voice quality in a zero-shot setting.
  arXiv  Detail & Related papers  (2023-04-18T16:31:59Z)
• Cost-effective Models for Detecting Depression from Speech [4.269268432906194]
  Depression is the most common psychological disorder and is considered as a leading cause of disability and suicide worldwide. A system capable of detecting signs of depression in human speech can contribute to ensuring timely and effective mental health care for individuals suffering from the disorder.
  arXiv  Detail & Related papers  (2023-02-18T02:46:21Z)
• Analyzing Robustness of End-to-End Neural Models for Automatic Speech Recognition [11.489161072526677]
  We investigate robustness properties of pre-trained neural models for automatic speech recognition. In this work, we perform a robustness analysis of the pre-trained neural models wav2vec2, HuBERT and DistilHuBERT on the LibriSpeech and TIMIT datasets.
  arXiv  Detail & Related papers  (2022-08-17T20:00:54Z)
• Self-supervised models of audio effectively explain human cortical responses to speech [71.57870452667369]
  We capitalize on the progress of self-supervised speech representation learning to create new state-of-the-art models of the human auditory system. We show that these results show that self-supervised models effectively capture the hierarchy of information relevant to different stages of speech processing in human cortex.
  arXiv  Detail & Related papers  (2022-05-27T22:04:02Z)
• Prediction of Depression Severity Based on the Prosodic and Semantic Features with Bidirectional LSTM and Time Distributed CNN [14.994852548758825]
  We propose an attention-based multimodality speech and text representation for depression prediction. Our model is trained to estimate the depression severity of participants using the Distress Analysis Interview Corpus-Wizard of Oz dataset. Experiments show statistically significant improvements over previous works.
  arXiv  Detail & Related papers  (2022-02-25T01:42:29Z)
• Utilizing Self-supervised Representations for MOS Prediction [51.09985767946843]
  Existing evaluations usually require clean references or parallel ground truth data. Subjective tests, on the other hand, do not need any additional clean or parallel data and correlates better to human perception. We develop an automatic evaluation approach that correlates well with human perception while not requiring ground truth data.
  arXiv  Detail & Related papers  (2021-04-07T09:44:36Z)
• Multimodal Depression Severity Prediction from medical bio-markers using Machine Learning Tools and Technologies [0.0]
  Depression has been a leading cause of mental-health illnesses across the world. Using behavioural cues to automate depression diagnosis and stage prediction in recent years has relatively increased. The absence of labelled behavioural datasets and a vast amount of possible variations prove to be a major challenge in accomplishing the task.
  arXiv  Detail & Related papers  (2020-09-11T20:44:28Z)

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.