CoLMbo: Speaker Language Model for Descriptive Profiling
This work addresses the need for more descriptive speaker profiling in audio processing, offering a novel method that enhances traditional systems but is incremental in its application to a specific domain.
The paper tackled the problem of speaker recognition systems being limited to classification and lacking detailed speaker descriptions by introducing CoLMbo, a Speaker Language Model that generates structured captions for demographic attributes like dialect, gender, and age, achieving strong performance in zero-shot scenarios across diverse datasets.
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.