Speaker Prompt Cache Mechanisms

• Speaker Prompt Cache (SPC) is a system that stores, retrieves, and fuses speaker-specific prompts, enabling real-time, speaker-adaptive processing across multiple modalities.
• It utilizes strategies like K-means clustering, tensor storage, and semantic search to construct and retrieve prompt experts for applications in ASR, visual speech recognition, and dialogue systems.
• Empirical results show that SPC reduces latency and storage overhead while significantly improving recognition accuracy and consistency in both streaming and static environments.

A Speaker Prompt Cache (SPC) is a structured mechanism for storing, retrieving, and dynamically fusing speaker-specific prompts or embeddings to facilitate fast, adaptive, and speaker-aware processing in deep learning systems. Recent implementations have demonstrated its utility in ASR, speaker diarization, and even conversational caching for spoken agents. SPCs are characterized by principled prompt construction, efficient storage and retrieval strategies, and real-time integration within model pipelines. Central themes include architecture-agnostic prompting, statistical or clustering-based expert selection, and on-the-fly cache updates that preserve system consistency and adaptation efficiency.

1. Core Principles and Variants of Speaker Prompt Cache

SPC encapsulates domain-specific and speaker-personalized information in the form of prompts. Its application spans several modalities:

• Audio-Centric SPC: Extracts hidden representations or embeddings from enrollment utterances and clusters them to form a compact set of "prompt experts." Typical implementations (e.g., MOPSA for Whisper) decouple encoder and decoder prompts and construct a latent cache by K-means clustering over prompt parameters, storing cluster centroids as speaker prompt experts (Deng et al., 30 May 2025).
• Visual SPC: In VSR, per-speaker prompts (addition, padding, concatenation) are stored as small numerical tensors—potentially across multiple network layers—to encode speaker idiosyncrasies in visual patterns (Kim et al., 2023).
• Conversational SPC: In dialogue agents (e.g., ConvoCache), an SPC stores embeddings of recent dialogue histories to expedite response retrieval by semantic similarity (Atkins et al., 26 Jun 2024).
• Streaming/Diarization SPC: In Speech-LLM-based diarization, SPC holds representative utterances (audio plus text) for each speaker, ensuring label consistency and identity preservation throughout streaming inference (Shi et al., 20 Nov 2025).

The invariants are (a) parameter-efficient encoding, (b) minimal model disruption, and (c) structure facilitating real-time or low-latency adaptation.

2. Construction and Learning of Speaker Prompt Experts

Construction of an SPC is strongly problem- and architecture-dependent, with key methodologies including:

• Feature Extraction and Prompt Generation: For each speaker, feature vectors (e.g., log-Mel spectrogram summaries) are computed and used as a basis for learning prompts via supervised loss minimization, often leveraging prompt tokens within encoder/decoder stacks (e.g., Whisper or CNN-Transformer backbones) (Deng et al., 30 May 2025, Kim et al., 2023).
• Clustering of Prompt Parameters: A set of learned per-speaker prompts is clustered (commonly with K-means) to identify a small number of representative "expert" prompts. For position-wise prompt vectors $P_e^{i,l}$ (encoder) or $P_d^{i,l}$ (decoder), clustering minimizes the L2 distance between individual prompts and cluster centroids $E_j^l$, $F_j^l$, stored as the SPC (Deng et al., 30 May 2025).
• Direct Prompt-Tuning: For visual models, prompts are optimized on speaker-specific adaptation data, with types including additive perturbations, replacement padding (for convolutional layers), or temporal concatenation of prompt tokens to Transformer features (Kim et al., 2023).

SPC entries can also consist of fixed-length paired audio-text utterances, as in streaming diarization systems, supporting identity persistence across sequence chunks (Shi et al., 20 Nov 2025).

3. Storage Formats and Retrieval Strategies

Efficient storage and retrieval are required for low-latency adaptation:

• Tensor Storage: Prompt experts are stored in contiguous tensors, e.g., $E_{\text{enc}}\in\mathbb{R}^{C\times L_e\times D}$ and $F_{\text{dec}}\in\mathbb{R}^{C\times L_d\times D}$ for encoder/decoder clusters respectively (Deng et al., 30 May 2025).
• Lookup Tables: For prompt-tuned VSR systems, the SPC is a simple table mapping speaker IDs to optimized prompt parameters (addition, padding, concatenation) (Kim et al., 2023).
• Semantic Indices: In conversational cache systems, the SPC maintains a FAISS-based semantic index associating embedded dialogue vectors with responses (and potentially pre-synthesized audio), enabling fast approximate nearest-neighbor search on each prompt (Atkins et al., 26 Jun 2024).
• Rolling and Updating Caches: For streamable diarization, the SPC consists of at-most-one (audio, text) pair per speaker, updated with each chunk using length and content quality checks and speaker embedding similarity thresholds (Shi et al., 20 Nov 2025).

4. Prompt Fusion, Routing, and Application

SPC retrieval is followed by prompt fusion and injection into the core model, using strategies tailored to the underlying neural architecture:

• Fusion by Weighted Mixture: A router network computes per-expert mixture weights $\alpha_j^l$ via a softmax over router outputs, facilitating dynamic fusion of multiple experts for each prompt position. The resulting fused prompt is injected into model encoder/decoder blocks (Deng et al., 30 May 2025).
• Direct Prompt Replacement or Extension: In models supporting per-user SPC (VSR or diarization), the cached prompt is directly prepended, added, or substituted at the appropriate layer or segment, with minimal computation on retrieval (Kim et al., 2023, Shi et al., 20 Nov 2025).
• Semantic Similarity Search and Re-Ranking: In conversational SPC, retrieval is done using cosine similarity between query prompt embeddings and stored conversation vectors, with results filtered for coherence using separately trained models (e.g., UniEval) and returned on the basis of confidence thresholds (Atkins et al., 26 Jun 2024).

5. Online, Real-Time Adaptation and Streaming Use Cases

SPC enables rapid and parameter-efficient adaptation:

• Zero-Shot Adaptation: Once the SPC is built, adaptation for an unseen speaker involves a short enrollment utterance, with the router and fusion steps incurring negligible computational overhead (~5 ms per speaker), and no additional model finetuning (Deng et al., 30 May 2025).
• Streaming Inference: For chunk-wise streaming ASR and diarization, the SPC maintains up-to-date caches, with updates triggered only under explicit quality or novelty criteria, maintaining constant storage and computational latency even for arbitrarily long sequences (Shi et al., 20 Nov 2025).
• Low-Latency Interactive Systems: In chatbots and conversational agents, SPC retrieval reduces response latency by over fourfold relative to full LLM+TTS pipelines, with cache hit rates exceeding 88% and near-human coherence filtered responses (Atkins et al., 26 Jun 2024).

6. Empirical Impact, Efficiency, and Trade-Offs

SPC mechanisms yield substantive improvements across modalities:

• ASR and Speaker Adaptation: In elderly speech recognition, SPC-enabled MOPSA achieves statistically significant absolute WER reductions of 0.86% (4.21% relative) and CER reductions of 1.47% (5.40% relative) over speaker-independent baselines, while reducing real-time factor by up to 16.12× versus offline batch-mode adaptation (Deng et al., 30 May 2025).
• Diarization Consistency: In streamable ASR/diarization, SPC enables globally consistent speaker identities with WDER reductions (e.g., from 2.09% to 1.73%) and outperforms cascaded systems such as DiarizationLM, with minimal cost in latency or storage (Shi et al., 20 Nov 2025).
• Visual Speech Recognition: SPC use reduces speaker-adaptive storage overhead to ≪ 1% per speaker (compared to 100% in fine-tuning), achieves up to 68% WER reduction, and matches or exceeds classical and full fine-tuning methods given modest adaptation data (Kim et al., 2023).
• Conversational Latency and Cost: SPC-based response retrieval in chatbots delivers 88–89% cache hit rates, average latencies around 214 ms, and up to 89% reduction in LLM+TTS invocation costs (Atkins et al., 26 Jun 2024).

7. Limitations, Implementation Choices, and Generalization

SPC design presents trade-offs:

• Storage vs. Accuracy: Storing more comprehensive or higher-dimensional prompts increases accuracy at modest storage cost, but even succinct SPCs suffice for effective adaptation (Kim et al., 2023, Deng et al., 30 May 2025).
• Update Strategies: For streaming use, cache updates must balance coverage (refreshing on true speaker changes) and stability (preventing false merges/drift), typically via conservative length, punctuation, and similarity heuristics (Shi et al., 20 Nov 2025).
• Applicability and Domain Constraints: SPC excels in scenarios requiring fast, lightweight speaker adaptation without catastrophic forgetting, and generalizes to other modalities/pipelines (e.g., chatbots, IVR systems). However, its effectiveness can be constrained by semantic drift (in conversational settings) or diminished gains in high-data regimes where full fine-tuning prevails (Atkins et al., 26 Jun 2024, Kim et al., 2023).

A plausible implication is that further advances in universal prompt construction and dynamic cache management could extend SPC to encompass broader task adaptation, cross-modal transfer, and more robust, safety-aware conversational reuse.

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References:

• "MOPSA: Mixture of Prompt-Experts Based Speaker Adaptation for Elderly Speech Recognition" (Deng et al., 30 May 2025)
• "Prompt Tuning of Deep Neural Networks for Speaker-adaptive Visual Speech Recognition" (Kim et al., 2023)
• "ConvoCache: Smart Re-Use of Chatbot Responses" (Atkins et al., 26 Jun 2024)
• "Train Short, Infer Long: Speech-LLM Enables Zero-Shot Streamable Joint ASR and Diarization on Long Audio" (Shi et al., 20 Nov 2025)