Phoneme-Level Energy Sequence Analysis

• Phoneme-level energy sequence is a representation that aggregates speech energy over phoneme boundaries, offering a semantically meaningful and controllable abstraction.
• The methodology extracts frame-level energy and averages it over forced-aligned segments, ensuring efficient and interpretable feature computation.
• Applications span expressive singing synthesis, robust speech enhancement, and deepfake detection, demonstrating significant improvements in accuracy and user control.

Phoneme-level energy sequence refers to the representation, extraction, modeling, and control of the temporal loudness (energy) contour aligned with the phonemic units of speech or singing. This concept is central to diverse speech and audio technologies, including expressive speech and singing voice synthesis, robust speech enhancement, prosody modeling, representation learning, and deepfake detection. Work on phoneme-level energy sequences draws from methods in signal processing, statistical modeling, and contemporary deep learning architectures.

1. Definition and Motivation

A phoneme-level energy sequence is a temporal sequence where each element corresponds to the energy (or loudness) associated with a specific phoneme segment within a spoken or sung utterance. Unlike frame-level representations, which capture instantaneous energy over very short frames (e.g., 10ms), the phoneme-level sequence aggregates or models energy per detected or forced-aligned phoneme, providing a semantically meaningful, user-friendly, and often more controllable abstraction.

This representation is crucial for:

• Precise user-driven dynamics control in synthesis ("Controllable Singing Voice Synthesis using Phoneme-Level Energy Sequence" (Ryu et al., 8 Sep 2025))
• Fine-grained analysis of speech enhancement impact at a linguistic unit level ("A Phoneme-Scale Assessment of Multichannel Speech Enhancement Algorithms" (Monir et al., 24 Jan 2024))
• Improved efficiency and alignment in end-to-end models ("Exploring Phoneme-Level Speech Representations for End-to-End Speech Translation" (Salesky et al., 2019))
• Detecting signal artifacts or inconsistencies in deepfake detection and robust speech recognition (Zhang et al., 17 Dec 2024)

2. Extraction and Computation of Phoneme-Level Energy

The derivation of a phoneme-level energy sequence typically involves two main steps: computing an energy estimate at the frame level and aggregating these values over phoneme boundaries.

Frame-level Energy Extraction:

• The energy for each spectrogram frame t is computed often as the root of the mean squared amplitude across mel frequency bins:

$$E[t] = \sqrt{ \frac{1}{N} \sum_{n} \exp(S_\text{mel}[t, n])^2 }$$

where $S_\text{mel}[t, n]$ is the log-mel spectrogram value for frame t and bin n (Ryu et al., 8 Sep 2025).

Phoneme-level Aggregation:

• For each phoneme $i$, bounded by frame indices $t_{start}^i$ and $t_{end}^i$, energy is aggregated as the mean:

$$e_i = \frac{1}{T_i} \sum_{t=t_{start}^i}^{t_{end}^i} E[t]$$

yielding a sequence $E = [e_1, e_2, \dots, e_L]$ for L phonemes.

• The phoneme alignment is typically achieved via forced alignment with trained phoneme recognizers, HMMs, or neural ASR models (e.g., Kaldi, Montreal Forced Aligner). The aggregation protocol mirrors the adaptive phoneme pooling approach in (Zhang et al., 17 Dec 2024), which shifts frame-wise features to phoneme-aligned average representations.

3. Integration in Speech and Singing Voice Synthesis

Phoneme-level energy sequences are used as explicit conditioning features to enable dynamic, interpretable control in generative models.

Controllable Singing Voice Synthesis:

• The SVS model ("Controllable Singing Voice Synthesis using Phoneme-Level Energy Sequence" (Ryu et al., 8 Sep 2025)) admits as input several phoneme-level sequences: lyrics, musical notes, durations, and energy.
• Embeddings of these sequences are summed prior to passing through a Feed-Forward Transformer (FFT), producing a context representation $H_c$, which is then used to condition a Denoising Diffusion Probabilistic Model (DDPM) for spectrogram generation:

$$h^m = h^l + h^n + h^e,\quad H_c = FFT(h^m),\quad D_\text{out} = DDPM(H_c),\quad \hat{Y} = \text{Vocoder}(D_\text{out})$$

• The phoneme-level energy sequence enables direct, user-friendly control of loudness dynamics: a user only needs to specify one value per phoneme for global contour control versus highly granular frame-level specification (Ryu et al., 8 Sep 2025).
• Results demonstrate a 57% reduction in mean absolute error (MAE) of energy contours compared to baseline and energy-predictor models, confirming that user-supplied phoneme-level energy input robustly determines dynamic expressiveness.

Fine-Grained Prosody and Expressive Speech Synthesis:

• Related work in TTS models, such as "Prosodic Clustering for Phoneme-level Prosody Control" (Vioni et al., 2021) and "Controllable speech synthesis by learning discrete phoneme-level prosodic representations" (Ellinas et al., 2022), illustrates that phoneme-level prosody features (including F0, duration, and potentially energy) can be discretized using unsupervised clustering. This results in a sequence of explicit labels used alongside phoneme identities, supporting both style transfer and speaker adaptation.

4. Analysis, Modeling, and Applications

Compression and Efficiency:

• Transforming frame-level speech sequences into phoneme-level representations via averaging frames within phoneme boundaries leads to 80% reduction in sequence length, faster training, and substantial BLEU improvements in speech translation tasks (Salesky et al., 2019).

Speech Enhancement and Phoneme-Specific Impact:

• Phoneme-level analysis of enhancement algorithms shows that classes with distinct energy contours, such as plosives (short, high-energy bursts) and sibilants (sustained high-frequency energy), respond differentially to noise and enhancement. Algorithms such as MVDR, FasNet, and Tango yield only modest SIR improvements for transient plosives but perform better for nasals and sibilants (Monir et al., 24 Jan 2024).

Deepfake Detection:

• Adaptive phoneme pooling is leveraged for averaging frame-level features into phoneme-level vectors, exposing inconsistencies in deepfake speech where the generation models fail to capture natural transitions of energy and other spectral features across phonemes. A graph attention network models the sequence of phoneme-level features, amplifying detection robustness (Zhang et al., 17 Dec 2024).

Representation Learning:

• Phoneme-level masking—masking entire phoneme segments rather than fixed-length spans—drives models to learn better representations of the energy envelope at the phoneme level, improving both phoneme classification and speaker recognition (Zhang et al., 2022).
• Mixed-Phoneme BERT introduces mixed phoneme and sup-phoneme tokens; such contextual encodings can potentially capture fine energy contours, offering improved prosody and naturalness in downstream TTS (Zhang et al., 2022).

Neural Prosody Modeling and Disentanglement:

• Discrete, phoneme-level latent spaces built from RVQ-VAE codecs can be structured so that principal components align with pitch or RMS energy. Such disentanglement ensures robust, interpretable, and transferable prosody editing or voice conversion (Karapiperis et al., 13 Sep 2024).

5. Experimental Evidence and Evaluation Metrics

Experiment/Application	Metric	Key Results
Singing voice synthesis w/ phoneme-level energy	MAE (energy); MOS	MAE reduced by 57%; MOS from 3.43→3.78 (Ryu et al., 8 Sep 2025)
Speech enhancement (phoneme level)	SIR, SAR (by phoneme class)	Sibilants improved most; plosives remain difficult (Monir et al., 24 Jan 2024)
Deepfake detection (pooled phoneme features)	AUC, t-SNE clustering	Clear separation by feature irregularities (Zhang et al., 17 Dec 2024)
Speech translation with phoneme input	BLEU, training time	Up to +5 BLEU, 60% time reduction (Salesky et al., 2019)

These experiments verify that phoneme-level energy modeling provides not only direct control and interpretability but also improved accuracy, robustness, and user efficiency compared to traditional methods.

6. Limitations and Research Directions

Dependency on Alignment Quality and Recognizer Accuracy:

• The ability to extract reliable phoneme-level energy sequences depends on the quality of forced alignment and underlying phoneme recognition, which can be challenging in low-resource or highly noisy settings (Salesky et al., 2019, Zhang et al., 2022).

Frame-level versus Phoneme-level Tradeoff:

• Phoneme-level representations, while user-friendly and semantically meaningful, may lose some fine-grained temporal precision present in frame-level modeling. However, empirical evidence indicates this loss is slight when considering perceptual quality and dynamic accuracy (Ryu et al., 8 Sep 2025).

Generalization and Disentanglement:

• Factorizing energy from phonetic and speaker information for universal control remains an active area, with recent advances in discrete latent spaces showing interpretability and transferability (Karapiperis et al., 13 Sep 2024).

Future Directions:

• Extending phoneme-level control to additional expressive parameters (e.g., timbre, vibrato) beyond energy and pitch (Ryu et al., 8 Sep 2025)
• Developing unsupervised or cross-lingual alignment and phoneme boundary detection for broader applicability (Zhang et al., 2022, Salesky et al., 2019)
• Leveraging user-driven or predictive priors to map from text/score directly into energy contours for zero-shot expressive control (Karapiperis et al., 13 Sep 2024)

7. Conclusion

Phoneme-level energy sequences constitute an important representational and control paradigm in speech and singing synthesis, enhancement, and analysis. By constraining and modeling energy at linguistically meaningful intervals, these sequences support dynamic, user-controllable prosody, facilitate more robust and efficient systems, and provide fine-grained analytical granularity for both research and practical applications. The methodology is validated across domains, with results indicating both increased expressive power and improved quantitative performance under diverse evaluation criteria. Ongoing research seeks to further refine alignment techniques, expand expressive control, and generalize these concepts across languages, modalities, and applications.