[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-327235-105":59,"doc-detail-327235-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","props-prompted-profile-synthesis-for-natural-language-conditioned-speaker-embedding-distributions-abstract","ProPS - Prompted Profile Synthesis for Natural Language-Conditioned Speaker Embedding Distributions - Abstract","","Speaker embeddings, commonly called x-vectors, are widely used to represent speaker identity and related attributes, but existing systems are mainly descriptive rather than generative. ProPS introduces Prompted Profile Synthesis, generating distributions of speaker embeddings conditioned on natural-language prompts that describe speaker profiles. ProPS maps profile descriptions to sentence embeddings and uses a mixture density network to predict a Gaussian mixture in x-vector space. Training maximizes likelihood that real embeddings match requested profiles; evaluation uses negative log-likelihood and attribute classification accuracies on held-out x-vectors and sampled synthetic ones. Results show generated x-vectors preserve requested attributes such as age, gender, accent, and prosody.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/props-prompted-profile-synthesis-for-natural-language-conditioned-speaker-embedding-distributions-abstract/327235/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/props-prompted-profile-synthesis-for-natural-language-conditioned-speaker-embedding-distributions-abstract/327235.png","ImageObject",300,407,{"name":92,"@type":93},"Aurelia","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",11,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What is ProPS (Prompted Profile Synthesis)?","Question",{"text":113,"@type":114},"ProPS is a framework for generating distributions of speaker embeddings conditioned on natural-language prompts describing a desired speaker profile.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How does ProPS use natural-language prompts to generate x-vector distributions?",{"text":118,"@type":114},"ProPS embeds the prompt text with a sentence encoder, then a mixture density network predicts a Gaussian mixture model in the x-vector space.",{"name":120,"@type":111,"acceptedAnswer":121},"How are the generated distributions evaluated?",{"text":122,"@type":114},"Evaluation uses negative log-likelihood on held-out x-vectors and downstream attribute classification accuracies on sampled synthetic x-vectors.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},327235,1790380165,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":34,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145,"preview_page_count":4},1099514068365,"https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1794309523707217728","ProPS: Prompted Profile Synthesis for Natural Language-Conditioned Speaker Embedding  \nDistributions  \nThomas Thebaud∗ , Junhyeok Lee∗ , Laureano Moro-Velazquez∗ , Jesus Villalba Lopez∗ , Najim Dehak∗∗ Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA  \nEmail: [tthebau1@jhu.edu](tthebau1@jhu.edu)  \narXiv :2607 .05276v1 [ ee ss .AS] 6 Jul 2026  \nAbstract—Speaker embeddings, or x-vectors, are widely used to represent speaker identity and speaker-related attributes, but existing embedding extractors are typically descriptive rather than generative: they map an observed speech segment to an x-vector, which is then used for downstream applications. We introduce ProPS, Prompted Profile Synthesis, a framework for generating distributions of speaker embeddings conditioned on natural language prompts such as ”a thirties male speaker with an Indian accent”. ProPS converts human-written profile descriptions into sentence embeddings and uses a mixture density network trained on a large-scale dataset to predict a Gaussian mixture model in the x-vector space. The model is trained by maximizing the likelihood that real speaker embeddings match the requested profile, and its generated distributions are evaluated by negative log-likelihood on held-out x-vectors and by attribute classification accuracies on sampled synthetic x-vectors. Experiments show that ProPS produces profile-conditioned distributions and generates x-vectors that preserve requested speaker attributes such as age, gender, accent, and prosodic characteristics. This design enables controllable speaker-profile synthesis for speech generation systems like Text-To-Speech (TTS) or Voice Conversion (VC) while anchoring generated distributionsin observed speaker-embedding structure.  \nIndex Terms—X-vectors, mixture density networks, controllable generation, synthetic speakers, natural language prompts  \nI. INTRODUCTION  \nHuman voices carry information beyond linguistic content, including speaker identity, demographic background, accent, speaking style, prosody, and recording conditions. A large body of work has studied how to extract, represent, compare, and profile speaker identities from speech signals. As speech technologies become increasingly generative and controllable, modeling not only individual identities but also broader speaker profiles becomes an important research problem.  \nNeural speaker embeddings, commonly referred to as xvectors [1], are now the dominant representation for speaker information. They map speech segments to fixed-dimensional vectors that capture speaker-related characteristics while abstracting away from the raw waveform. X-vectors and related embeddings are widely used in speaker verification [1], diarization [2], voice conversion [3], anonymization [4], textto-speech [5], speaker profiling [6], and healthcare applications [7] . In generative systems, they often serve as compact conditioning variables for synthesizing, converting, or adapting speech toward a desired voice identity.  \nWhen reference audio is available, an enrollment utterance can be used to extract the desired x-vector. Without such audio, controllable generation of new speaker identities remains limited. Existing approaches often sample synthetic embeddings from distributions fitted to training x-vectors, such as Gaussian mixture models or PLDA-style priors. While these methods can generate new points in speaker space, their controllability is constrained by the labels, structure, and biases of the data used to estimate the distribution.  \nWe propose ProPS, Prompted Profile Synthesis, a framework for natural language-conditioned generation of x-vector distributions. Instead of sampling x-vectors from an unconditional or weakly conditioned speaker prior, ProPS takes a text prompt describing a speaker profile (such as ”a young female speaker with an Indian accent and a fast pace”) and generates a Gaussian mixture model in the x-vector spac","cbCaighZVLWHEKRr","https://ap.wps.com/l/cbCaighZVLWHEKRr","pdf",1504434,"English","# Introduction\n## Speaker embeddings and their use\n## Limitations of existing controllable generation\n## Proposed approach and contributions\n# Related Work\n## Speaker Embeddings","[{\"question\":\"What is ProPS (Prompted Profile Synthesis)?\",\"answer\":\"ProPS is a framework for generating distributions of speaker embeddings conditioned on natural-language prompts describing a desired speaker profile.\"},{\"question\":\"How does ProPS use natural-language prompts to generate x-vector distributions?\",\"answer\":\"ProPS embeds the prompt text with a sentence encoder, then a mixture density network predicts a Gaussian mixture model in the x-vector space.\"},{\"question\":\"How are the generated distributions evaluated?\",\"answer\":\"Evaluation uses negative log-likelihood on held-out x-vectors and downstream attribute classification accuracies on sampled synthetic x-vectors.\"}]","ProPS - Prompted Profile Synthesis for Natural Language-Conditioned Speaker Embedding Distributions - Abstract | PDF",1789976776,18]