The delayed access to specialized psychiatric assessments and care for
patients at risk of suicidal tendencies in emergency departments creates a
notable gap in timely intervention, hindering the provision of adequate mental
health support during critical situations. To address this, we present a
non-invasive, speech-based approach for automatic suicide risk assessment. For
our study, we have collected a novel dataset of speech recordings from $20$
patients from which we extract three sets of features, including wav2vec,
interpretable speech and acoustic features, and deep learning-based spectral
representations. We proceed by conducting a binary classification to assess
suicide risk in a leave-one-subject-out fashion. Our most effective speech
model achieves a balanced accuracy of $66.2\,\%$. Moreover, we show that
integrating our speech model with a series of patients' metadata, such as the
history of suicide attempts or access to firearms, improves the overall result.
The metadata integration yields a balanced accuracy of $94.4\,\%$, marking an
absolute improvement of $28.2\,\%$, demonstrating the efficacy of our proposed
approaches for automatic suicide risk assessment in emergency medicine.

为了解决在紧急医学中对存在自杀风险的患者延迟专科精神评估和护理的问题，我们提出了一种非侵入性的基于语音的自动自杀风险评估方法，结合录音数据和元数据集成，有效提高了自动自杀风险评估的准确性。

增强自杀风险评估：应用语音技术的自动化方法于急症医学

Enhancing Suicide Risk Assessment: A Speech-Based Automated Approach in  Emergency Medicine

We investigate the emergent abilities of the recently proposed web-scale
speech model Whisper, by adapting it to unseen tasks with prompt engineering.
We selected three tasks: audio-visual speech recognition (AVSR), code-switched
speech recognition (CS-ASR), and speech translation (ST) on unseen language
pairs. We design task-specific prompts, by either leveraging another
large-scale model, or simply manipulating the special tokens in the default
prompts. Experiments show that compared to the default prompts, our proposed
prompts improve performance by 10% to 45% on the three zero-shot tasks, and
even outperform SotA supervised models on some datasets. In addition, our
experiments reveal many interesting properties of Whisper, including its
robustness to prompts, bias on accents, and the multilingual understanding in
its latent space. Code is available at
this https URL

本文通过调整 Prompt 的方式，从三个任务 (音视频语音识别、混合语音识别、语音翻译) 入手，探究了该模型 Whisper 的应用性能。实验证明，相对于默认 Prompt，本文提出的 Prompt 在零 - shot 任务上的表现提升了 10% 到 45%，并在一些数据集上甚至超越了 SotA 监督模型。此外，实验还揭示了 Whisper 的许多有趣属性，例如其对提示的鲁棒性、对语音口音的偏见，以及在潜在空间中的多语言理解。

发掘 Web 规模语音模型的潜在能力，实现零样本任务的普适性

Prompting the Hidden Talent of Web-Scale Speech Models for Zero-Shot  Task Generalization

We present a novel way of conditioning a pretrained denoising diffusion
speech model to produce speech in the voice of a novel person unseen during
training. The method requires a short (~3 seconds) sample from the target
person, and generation is steered at inference time, without any training
steps. At the heart of the method lies a sampling process that combines the
estimation of the denoising model with a low-pass version of the new speaker's
sample. The objective and subjective evaluations show that our sampling method
can generate a voice similar to that of the target speaker in terms of
frequency, with an accuracy comparable to state-of-the-art methods, and without
training.

本文提出了一种新的方法，通过采样识别新目标的自然语音数据，并在推理期间利用加噪扩散语音模型生成具有目标讲话者相似声音的音频，而不需要进行任何训练步骤。