Researcher · ML engineer_
1Multimedia Technologies Group (GTM), atlanTTic Research Center, Universidade de Vigo
I am a speech and language technology researcher finishing my PhD at the Universidade de Vigo, and an engineer who ships. I build models that read health from the voice: Long COVID, respiratory disease, emotional distress and suicide risk. In industry, I delivered the voice classifier for the COPERIA screening platform at Bahía Software, and I now advise Balidea on agentic AI systems for the Galician Health Service (SERGAS). My focus now is making generative AI that talks with vulnerable people aligned, robust and hard to misuse.
Keywords — speech biomarkers; multimodal learning; self-supervised speech representations; AI safety; alignment; safeguards for generative AI; mental-health AI.
| Line | Modality | Refs. |
|---|---|---|
| Voice as a clinical signal | [1][4][6] | |
| Mental-health AI and conversational safeguards | [2][3][5] | |
| Robust speech recognition and spoken search | [9][10][11][12][13][14] | |
| Language technology for under-resourced languages | [7][8] | |
| Alignment and safeguards for generative AI (current) | — |
Vera-López Á. et al. · Frontiers in Medicine · 2026
Vera-López Á., Tilves-Santiago D., Ramírez-Sánchez J.M., Docío-Fernández L., García-Mateo C., Bustillo-Casado M., García-Caballero A.A. Improving respiratory disease detection through SSL-enhanced acoustic analysis and exercise-rest measurements. Frontiers in Medicine, 2026.
A chat agent designed to spot suicide risk factors during a live conversation and to respond safely when it finds them.
Ramírez Sánchez J.M. et al. · IWSDS · 2025
Ramírez Sánchez J.M., Manso Vázquez M., García-Mateo C., Docío-Fernández L., Fernández-Iglesias M.J., Gómez-Gómez B., Pinal B., Brañas A., García-Caballero A. Design of a conversational agent to support people on suicide risk. IWSDS 2025 (International Workshop on Spoken Dialogue Systems Technology), 2025.
Ramírez Sánchez J.M. et al. · JMIR Preprints · 2023
Ramírez Sánchez J.M., Docío-Fernández L., García Mateo C., Bustillo-Casado M., García-Caballero A.A. Identifying patients with Long COVID: study of the effectiveness of voice signal analysis and machine learning. JMIR Preprints, 2023.
Ramírez Sánchez J.M. et al. · Revista Ingeniería Electrónica · 2019
Ramírez Sánchez J.M., Montalvo Bereau A.R., Calvo de Lara J.R. Evaluación de Rasgos Acústicos para el Reconocimiento Automático del Habla en Escenarios Ruidosos usando Kaldi. Revista Ingeniería Electrónica, Automática y Comunicaciones (RIELAC) 40(3), 2019.
Build the two classic speech features step by step in NumPy, watch one frame of real speech travel through both pipelines, and see what noise does to them. The front end every recogniser used before deep learning, and the subject of my first paper.
A complete, reproducible fine-tuning loop for Whisper on real speech, with grouped cross-validation, Optuna pruning, nested MLflow runs and a locked test set, ending in an explicit decision against a zero-shot baseline.
Freeze Whisper, inject LoRA adapters in the right places, and train under 2% of the parameters, either for an end-to-end classifier or for embeddings you feed to your own classifier.
Open a safety guard on a laptop CPU: read its verdict as a probability, watch the decision form layer by layer, and see why a harmful-content guard lets prompt injection hidden in documents straight through.
I audited four candidate guards for an agentic assistant layer by layer. They all decide in one token and late in the network, and the two dedicated guards miss prompt injection hidden in documents. A small generalist catches almost everything if it writes its justification before the verdict.
Where the policy lives, which layers read it, what the router does and why the last block can overturn a verdict: a tour through the internals of four LLM guards, measured on the weights they actually run.