ProMode Encoder: Video & Speech Optimization

• ProMode Encoder is a domain-specific architecture that optimizes video coding mode decisions and speech prosody modeling through actor models and transformer-based designs.
• It utilizes multi-objective optimization to balance bitrate, distortion, and energy consumption, achieving significant encoding time reductions and efficiency gains.
• The system integrates contextual features using strategic masking and cross-attention, which enhances performance in real-time video encoding and natural-sounding speech synthesis.

ProMode Encoder refers to domain-specific encoder architectures designed to optimize information representation for either video coding mode decisions or speech prosody modeling, as exemplified in works on HEVC encoder optimization (Herglotz et al., 2022) and speech prosody embedding (Eren et al., 12 Aug 2025). These encoders are characterized by principled feature integration, strategic masking, and multi-objective optimization, with implementations spanning actor models in system-level video encoding frameworks and transformer-based architectures in speech modeling. The “ProMode” designation highlights the focus on mode-related operations—either in video block coding or temporal-prosodic feature extraction for speech synthesis.

1. Architectural Foundations

Two distinct ProMode Encoder frameworks are documented: one targeting HEVC mode decision acceleration and energy efficiency (Herglotz et al., 2022), and another for contextualized speech prosody representation (Eren et al., 12 Aug 2025).

In HEVC, the ProMode Encoder is realized within a SystemC-based actor model, where separate mode evaluation functions (actors) operate on permutation and guard vectors ($\mathcal{O}$ and $\mathcal{G}$) to adaptively schedule and skip coding decisions. For speech prosody, the encoder is built atop the Perceiver IO architecture; input features consist of Mel-spectrograms, F0, energy, voiced/unvoiced flags, phoneme durations, and time-aligned textual embeddings merged and processed via cross-attention and transformer modules. Blockwise masking is applied at phoneme boundaries to enforce context-driven feature inference.

Domain	Input Feature Types	Architectural Basis
Video	Mode indices, permutation & guard vectors	SystemC actor model
Speech	Acoustic features, phoneme-aligned text, masking	Perceiver IO + ConvNeXt V2

These approaches leverage explicit modeling of both temporal ordering and contextual relationships, with highly modular designs suitable for integration and extension.

2. Multi-Objective Optimization and Conditioning

Optimization strategies in ProMode Encoders involve the balancing of several objectives and the use of sophisticated conditioning mechanisms. In HEVC, four criteria—bitrate, distortion, encoding time, and decoding energy—are jointly considered. The encoder’s search space is parameterized by permutations of mode evaluations and early skip guard conditions. A multi-objective evolutionary algorithm employing fast non-dominated sorting [Deb, 2002] explores Pareto-optimal trade-offs, e.g., $>60\%$ encoding time reduction or $\sim3\%$ decoding energy savings at the expense of a $\sim3\%$ bitrate increase.

In the speech model, the encoder maps joint acoustic-textual signals to prosodic feature spaces (F0, energy) under heavy masking. Conditioning is implemented using a modified adaLN-zero module that introduces an additional cross-attention mechanism to predict time-dependent scale and shift parameters ($T{\times}6$ for sequence length $T$). This allows dynamic, temporally-sensitive adjustment of transformer activations for prosody prediction.

Key training losses include:

• $L_1$ (F0 regression)
• MSE (energy, Mel-10 filters)
• Binary cross-entropy (voiced/unvoiced flag)

Dynamic time warping is used for precise ground truth alignment.

3. Input Processing and Feature Engineering

Video ProMode Encoders process mode decision vectors ($\mathcal{O}$: execution order, $\mathcal{G}$: guard conditions) for each coding depth. Each actor (mode function) is executed only if early skip criteria are met, reflecting the best mode so far in rate-distortion space.

Speech ProMode Encoders require length-regulated phoneme-aligned textual content (derived via a grapheme-to-phoneme model and forced alignment) and a suite of acoustic features extracted with noise-robust pitch estimation (RMVPE). Features are projected into a shared embedding space using learned linear layers, with convolutional (ConvNeXt V2) processing for capturing longer-range temporal relationships prior to Perceiver encoding. Speaker embeddings (from ECAPA2) provide additional conditioning for personalization and invariance.

Acoustic feature extraction details:

• Audio bandwidth: 8 kHz
• Frame rate: 11.6 ms
• Mel-spectrograms: 10 lowest filters (<380 Hz)
• Phoneme durations: Montreal forced aligner

4. Evaluation Metrics and Benchmarking

ProMode Encoders are empirically benchmarked against state-of-the-art baselines:

For HEVC:

• Encoding time reduction (%)
• BD-rate increase (%)
• Decoding energy savings (%); computed as $\hat{E} = \sum_f n_f \cdot e_f$ with $n_f$ the occurrences and $e_f$ the energy cost per bit stream feature.

For speech:

• F0 accuracy: raw pitch accuracy (RPA), raw chroma accuracy (RCA), RMSE, MAE
• Energy prediction: MAE, logarithmic MAE ($\mathrm{MAE}_{\log}$)
• Downstream TTS performance: word error rate (WER), UTMOS (naturalness), AutoPCP (prosody similarity)
• Perceptual ABX tests for listener prosody preference

ProMode consistently outperforms counterparts (StyleTTS2, Wav2Vec2-SER, Emotion2Vec) in prosody prediction accuracy as well as in downstream synthesis naturalness and intelligibility.

5. Integration Pathways and Applications

The video ProMode Encoder facilitates practical design space exploration for real-time encoders tailored to energy-sensitive devices. The actor model’s modularity enables straightforward experimentation with parallelization scenarios and fine-granularity mode splitting, suggesting extensions for server-side and client-side deployment.

In speech, ProMode is a stand-alone zero-shot prosody model. It produces prosodic embeddings from reference audio and predicts pitch/energy for new utterances based on text, enabling direct substitution for pitch predictors in TTS systems. Integration into FluentSpeech demonstrates improvements in word-level intelligibility, naturalness, and speaker/prosody similarity. The model can readily be extended to applications demanding fine-grained prosodic control, such as speech editing, expressive TTS synthesis, and multi-lingual adaptation.

6. Context, Limitations, and Future Directions

A salient limitation noted is input feature sensitivity: ablation studies reveal that the removal of components (e.g., Mel-spectrogram, duration, AOL) compromises accuracy, suggesting further robustness research is warranted. Current temporal conditioning (modified adaLN-zero) may benefit from more complex context modeling. In HEVC, ongoing work could refine actor granularity, parallel execution models, and guard heuristics for enhanced performance.

Future directions for speech ProMode include expanding to other languages, broader speech tasks such as editing, and richer prosodic feature sets beyond F0/energy. The actor-based video encoder may be adapted for more heterogeneous multi-core architectures or further automated trade-off exploration. A plausible implication is that ProMode Encoder methodologies, by jointly leveraging information-rich input spaces and multi-objective conditioning, provide a template for balanced, high-throughput, and energy-efficient representation learning in both video and speech domains.