CoLMbo: Speaker Language Model for Descriptive Profiling
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866908499830636544 |
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| author | Baali, Massa Han, Shuo Hannan, Syed Abdul Samal, Purusottam Singh, Karanveer Deshmukh, Soham Singh, Rita Raj, Bhiksha |
| author_facet | Baali, Massa Han, Shuo Hannan, Syed Abdul Samal, Purusottam Singh, Karanveer Deshmukh, Soham Singh, Rita Raj, Bhiksha |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_09375 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CoLMbo: Speaker Language Model for Descriptive Profiling Baali, Massa Han, Shuo Hannan, Syed Abdul Samal, Purusottam Singh, Karanveer Deshmukh, Soham Singh, Rita Raj, Bhiksha Computation and Language Sound Audio and Speech Processing 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. |
| title | CoLMbo: Speaker Language Model for Descriptive Profiling |
| topic | Computation and Language Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2506.09375 |