# CoLMbo: Speaker Language Model for Descriptive Profiling Source: [CoLMbo: Speaker Language Model for Descriptive Profiling]() Collection index: [llms.txt]() Data updated: 2025-10-27T07:00:40.261Z Authors: Massa Baali, Shuo Han, Syed Abdul Hannan, Purusottam Samal, Karanveer Singh, Soham Deshmukh, Rita Singh, Bhiksha Raj Published: 2025-06-11T03:50:16.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Speaker Profiling on VoxCeleb1-O](): Speaker Profiling; VoxCeleb1-O; Gender Accuracy: 78.6. ## Original sources and implementation [Original paper]() [PDF]()