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arXiv · 2506.09375

CoLMbo: Speaker Language Model for Descriptive Profiling

Massa Baali·Shuo Han·Syed Abdul Hannan·Purusottam Samal·Karanveer Singh·Soham Deshmukh·Rita Singh·Bhiksha Raj
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Abstract

Speaker recognition systems are often limited to classification tasks and struggle to generate detailed speaker characteristics or provide context-rich descriptions. These models primarily extract embeddings for speaker identification but fail to capture demographic attributes such as dialect, gender, and age in a structured manner. This paper introduces CoLMbo, a Speaker Language Model (SLM) that addresses these limitations by integrating a speaker encoder with prompt-based conditioning. This allows for the creation of detailed captions based on speaker embeddings. CoLMbo utilizes user-defined prompts to adapt dynamically to new speaker characteristics and provides customized descriptions, including regional dialect variations and age-related traits. This innovative approach not only enhances traditional speaker profiling but also excels in zero-shot scenarios across diverse datasets, marking a significant advancement in the field of speaker recognition.

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Massa Baali, Shuo Han, Syed Abdul Hannan, Purusottam Samal, Karanveer Singh, Soham Deshmukh, Rita Singh, Bhiksha Raj. 2025-08-23. CoLMbo: Speaker Language Model for Descriptive Profiling. https://arxiv.org/abs/2506.09375

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