ProPS: Prompted Profile Synthesis for Natural Language-Conditioned Speaker Embedding Distributions
Abstract
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 distributions in observed speaker-embedding structure.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper introduces ProPS, a novel framework for natural language-conditioned generation of speaker embedding distributions using Mixture Density Networks initialized from profile-specific GMMs, enabling controllable and diverse synthetic speaker creation for downstream speech applications.
Methodology
The paper proposes ProPS, a framework for generating speaker embedding distributions conditioned on natural language prompts. The core methodology involves using a Sentence-BERT encoder to map text prompts to embeddings, which are then fed into a Mixture Density Network (MDN). The MDN predicts the parameters (weights, means, diagonal covariances) of a Gaussian Mixture Model in the x-vector space. A key technical contribution is the initialization strategy: instead of random initialization, the authors pre-compute GMMs for each speaker profile in the dataset (CapSpeech) to create a "component bank," which initializes the MDN's Gaussian parameters. The training occurs in two stages: pre-training the MLP to map GPT-generated descriptions to the correct profile GMMs, and fine-tuning end-to-end with human-written descriptions to align with real x-vector distributions. This approach is technically sound and leverages established components (SBERT, MDN, ECAPA-TDNN) in a novel configuration for speaker profile synthesis.
Results & evaluation
The experiments are conducted on the CapSpeech dataset, which provides structured metadata and human-written descriptions. The evaluation metrics include Negative Log-Likelihood (NLL) on held-out x-vectors and attribute classification accuracy on sampled synthetic x-vectors. The ablation study effectively demonstrates the contribution of each training stage. Results show that ProPS successfully preserves demographic attributes (gender, accent, age) and some prosodic features, though prosodic control (pitch, pace, tone) is less reliable. The LDA visualizations provide qualitative support for the distributional match. The evaluation is rigorous within the scope of embedding-space generation, though it lacks downstream waveform generation tests which are mentioned as future work.
Reproducibility
The paper provides a GitHub link for the code (anonymized version). The dataset (CapSpeech) is publicly available. The methodology is described in sufficient detail, including hyperparameters (learning rates, epochs, K=16 components) and model architectures (SBERT all-MiniLM-L6-v2, ECAPA-TDNN). The use of GPT-4 (referred to as gpt-5.4-mini-2026-03-17, likely a typo or future-dated placeholder in the anonymized submission) for generating synthetic descriptions is noted, which aids reproducibility of the training data generation.
Limitations
The authors acknowledge several limitations: reliance on CapSpeech's label quality and coverage, potential dataset biases, and restriction to English data. The model struggles with fine-grained prosodic characteristics, which is partly inherent to the x-vector representation itself. The use of GPT-generated descriptions for pre-training introduces a potential distribution shift if the synthetic descriptions do not perfectly capture the semantic nuance of human descriptions, although the fine-tuning stage mitigates this.
Broader impact
ProPS enables controllable speaker synthesis for TTS and Voice Conversion systems without requiring reference audio, which has significant implications for accessibility, entertainment, and privacy (anonymization). By generating distributions rather than single points, it allows for diversity in generated voices, reducing the risk of generating only stereotypical or "average" voices. However, the ability to generate realistic speaker embeddings from text prompts also raises concerns about deepfake audio generation and impersonation, necessitating careful ethical guidelines and watermarking in downstream applications. The paper introduces ProPS, a novel framework for natural language-conditioned generation of speaker embedding distributions using Mixture Density Networks initialized from profile-specific GMMs, enabling controllable and diverse synthetic speaker creation for downstream speech applications.
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