Listener-Dependent Embeddings

• Listener-dependent embeddings are vector representations encoding listener identities and preferences to enable adaptive personalization in multimodal systems.
• They integrate with deep learning architectures using methods such as discrete lookups, tensor fusion, and cross-modal attention for improved task performance.
• Empirical studies show these embeddings boost metrics in tasks like backchannel prediction, music recommendation, and subjective quality estimation.

Listener-dependent embeddings are vector representations that encode the identity, preferences, perceptual biases, or behavioral history of a listener within multimodal or interactive machine learning architectures. These embeddings provide a latent parameterization of the listener that influences downstream tasks such as contrastive captioning, backchannel prediction, music recommendation, subjective quality estimation, dyadic impression recognition, and controllable avatar animation. Multiple methodologies have emerged across vision-language, speech, and affective computing domains, leveraging listener-dependent embeddings to improve personalization, adaptivity, and communicative effectiveness.

1. Mathematical Formulations of Listener-Dependent Embeddings

At their core, listener-dependent embeddings are learnable, fixed-dimensional vectors $e_\ell \in \mathbb{R}^d$ associated with each listener identity $\ell$. These embeddings can be as simple as lookups from a trainable matrix $E \in \mathbb{R}^{M \times d}$ indexed by unique listener IDs (typical $d=5\dots 128$), or as complex as functions of user histories, demographics, or inferred perceptual profiles.

Common instantiations:

• Discrete embedding table: For a set of $M$ listeners, $E[l,:]$ yields a $d$-dim embedding for listener $l$ (Ortega et al., 2023, Ortega et al., 2023, Huang et al., 2021, Li et al., 2022).
• Aggregated user representations: For recommendation, the listener embedding $u_i$ is aggregated from multiple context vectors of songs, albums, artists, and demographics, possibly concatenated and projected (contextual embedding) (Chen et al., 2020).
• Task-conditional adaptive embeddings: In vision–LLMs, the embedding is constructed on-the-fly via an adapter conditioned on perceptual divergences between listener models (Singh et al., 2022).

Once looked up or computed, listener-dependent embeddings are injected into downstream architectures, either fused at the input, through multimodal blocks, or in late-stage prediction layers.

2. Integration Architectures and Fusion Mechanisms

The integration of listener-dependent embeddings into neural architectures varies by domain and task:

a) Input Concatenation and Early Fusion

• Listener embedding $e_\ell$ is concatenated with other contextual inputs (e.g., acoustic, visual features) before being passed to the predictive layers (Ortega et al., 2023, Ortega et al., 2023, Huang et al., 2021, Li et al., 2022).
• Typical fusion example: $z = \text{concat}(h, u_\ell)$, where $h$ is a feature vector and $u_\ell$ is the listener embedding.

b) Nonlinear and Tensor Fusion

• Bilinear or tensor-based fusion models, such as neural tensor networks (NTN), model higher-order interactions between speaker and listener embeddings (Ortega et al., 2023):

$$\text{NTN}(s, \ell) = \tanh(s^\top W^{[1:k]}\ell + V[s;\ell] + b)$$

c) Cross-Modal Attention and Adaptation

• Speaker and listener representations are simultaneously encoded, and multi-head self/inter-attention blocks are deployed for effective cross-domain fusion (Li et al., 2022).
• Adaptation of speaker features to listeners is implemented via PWCCA segmental similarity weighting:

$$S^W = [w_1 s_1, \dots, w_n s_n], \quad w_i = \text{PWCCA}(s_i, \ell_i)$$

• MLPs, RNNs, or attention-rich fusions allow the architecture to capture subtle, personalized response patterns.

d) Listener as Model Parameter / Conditioning

• In vision-language pragmatic settings, listener embeddings shape the generation process by scoring candidate utterances or conditioning adapted encoders (e.g., via special CLIP adapter weights) (Ou et al., 2023, Singh et al., 2022).

3. Learning Paradigms and Losses

Listener-dependent embeddings are typically learned through end-to-end gradient-based optimization targeting performance metrics that depend directly on listener identification or modeled preference.

a) Supervised Classification or Regression

• In backchannel prediction, embeddings are trained jointly with network weights via cross-entropy over backchannel classes (Ortega et al., 2023, Ortega et al., 2023).
• For subjective quality estimation (e.g., MOS), per-listener MSE loss is used:

$$\mathcal{L}_{LD} = \frac{1}{N\,m} \sum_{i=1}^N \sum_{j=1}^m \left(y_{ij} - s_{ij}\right)^2$$

with the model output $y_{ij} = f(x_i, \ell_j)$ (Huang et al., 2021).

b) Metric and Contrastive Learning

• In recommender systems, metric learning aligns audio embeddings with listener-derived preference vectors via margin-based hinge loss:

$$\mathcal{L}_{\text{audio}}(u_i; r^+, \{r^-_j\}) = \sum_{j=1}^n \max\left[ 0, \Delta - M(r^+, u_i) + M(r^-_j, u_i) \right]$$

where $M(r, u_i)$ is the cosine similarity (Chen et al., 2020).

• In multi-agent language games, the reward function directly encodes the relative probability difference under different listeners, learned via policy gradient:

$r(x, u) = p_1(y|x, u) - p_2(y|x, u)$

(Singh et al., 2022).

c) Multi-objective and Regularized Training

• In impression recognition, a composite loss aggregates MSE on multiple target dimensions (e.g., warmth, competence) plus knowledge distillation and similarity enhancement regularizers to align listener and speaker latent spaces (Li et al., 2022).
• Emotional/dynamic tasks likewise blend reconstruction, velocity, and emotion-matching losses (e.g., VividListener (Li et al., 30 Apr 2025)).

4. Core Applications and Empirical Advancements

Listener-dependent embeddings have demonstrated marked improvements in diverse domains:

a) Vision-and-Language Pragmatics

• In contrastive captioning, listener-dependent scoring via CLIP embeddings boosts caption informativity by 11–15% absolute accuracy relative to prior methods, as measured by human retrieval; fluency remains high over a wide hyperparameter range (Ou et al., 2023).

b) Speech and Paralinguistics

• In backchannel prediction, listener embeddings increase test accuracy by 2–2.4% absolute (acoustic/lexico-acoustic settings) and boost F1 from 0.42 to 0.55 on challenging datasets (Ortega et al., 2023, Ortega et al., 2023).
• Modeling speaker-listener joint representations with tensor fusion further enhances macro-F1 and personalization.

c) Subjective Audio Quality

• LDNet delivers improved mean opinion score (MOS) prediction: introducing listener-embedding reduces utterance-level variance and captures “harsh-vs-lenient” rater tendencies (Huang et al., 2021). Mean-listener inference enables practical O(1) scoring at inference.

d) Music Personalization

• Listener embedding–audio embedding metric learning achieves precision up to 0.773 and AUC 0.849 in large-scale recommendation (Chen et al., 2020); cold-start and cross-task generalization are boosted via content-aware representations.

e) Dyadic Affect Modeling

• Listener-adaptive cross-domain fusion—using per-listener ID-embeddings and attention-based joint modeling—yields CCC >77% for impression prediction, outperforming prior art (Li et al., 2022).
• Fine-grained control of head/behavior dynamics in avatars is realized by RIM/EIT-based listener embeddings, enhancing responsiveness and expressivity (Li et al., 30 Apr 2025).

5. Design Considerations, Limitations, and Ablations

Embedding Dimension and Regularization

• Empirically, $d = 5$ suffices for backchannel and small-dataset tasks; $d \in [16, 64]$ is optimal for large-scale subjective estimation and cross-modal domains. $d > 64$ leads to overfitting unless regularized (Huang et al., 2021).
• Per-listener embeddings tend to cluster along axes of behavioral or perceptual bias (e.g., response preference, harshness of ratings) (Huang et al., 2021, Ortega et al., 2023).

Initialization and Training

• Random initialization of embeddings is standard, with embedding parameters trained jointly with core model weights.
• For transfer to new listeners, best practice is to fix encoders and update only new listener embedding vectors given small calibration sets (Huang et al., 2021).

Fusion Schemes

• Joint speaker-listener fusion using neural tensor networks or sum/bilinear fusion yields consistent performance gains, with NTNs often best on F1/CCC (Ortega et al., 2023, Li et al., 2022).

Computational Scaling and Inference

• All-listeners inference (mean over all known listeners) is $O(M)$; mean-listener embedding reduces this to $O(1)$ (Huang et al., 2021).
• In recommender settings, dual indexing (by track and by user) enables fast retrieval via precomputed embeddings (Chen et al., 2020).

Ablation Insights

• Removal of listener-embedding components (e.g., in backchannelers, MOS predictors) always leads to a measurable drop in accuracy, precision, or F1.
• In systems such as VividListener, ablation of interaction modules, emotional tags, or cross-modal attention results in significant degradation of frame/sequence diversity metrics and responsiveness (Li et al., 30 Apr 2025).

6. Broader Implications and Future Directions

Listener-dependent embeddings furnish a flexible, scalable mechanism for personalizing interactive AI systems. Their modularity supports plug-and-play integration into sequence models, cross-modal transformers, and metric-based recommenders.

Open directions include:

• Capturing time-varying or context-specific listener attributes (beyond static embeddings).
• Generalizing to unseen or cold-start listeners via meta-learning or few-shot adaptation.
• Exploring the interpretability and behavioral correlates of learned listener representations (e.g., what axes explain rating divergence or behavioral idiosyncrasies).
• Scaling to enormous, dynamic listener pools in production dialog or recommendation systems with billions of users.

Limitations around generalization to new listeners, overfitting in small data regimes, and the functional decomposition of listener versus context variables remain under active investigation. The recurring finding across modalities is that robust, light-weight listener-dependent embeddings—properly designed, modularly fused, and jointly trained—yield substantial improvements in personalization, discrimination, and user alignment in human-centered machine learning systems.