Evaluating Emotion Consistency in Long-Form TTS

Evaluating emotional consistency in expressive Text-to-Speech (TTS) across long passages requires measuring how well a synthesized voice maintains, transitions, and sustains targeted emotional states over time. Researchers use a combination of subjective human listening evaluations, objective acoustic analyses, and automated Speech Emotion Recognition (SER) embeddings to detect prosodic decay, emotional drift, and abrupt tone mismatches across sentence and paragraph boundaries.

Subjective Listening Tests

Human perception remains the primary benchmark for expressive TTS evaluation, though traditional single-sentence Mean Opinion Score (MOS) tests are inadequate for long-form audio. To evaluate extended passages, researchers adapt perceptual frameworks:

• Long-Form and Comparative MOS: Listeners rate multi-sentence passages on dedicated scales assessing emotional stability, naturalness, and appropriateness relative to the narrative context.
• Continuous Emotion Tracking: Evaluators use dynamic interfaces (such as time-continuous sliders along Valence-Arousal axes) to annotate their emotional perception in real time as the audio plays, pinpointing precisely where emotional flattening or drift occurs.
• ABX Preference Testing: Evaluators compare different model outputs across long paragraphs to identify which system sustains the intended mood without reverting to a neutral reading style.

Automated Speech Emotion Recognition (SER)

To scale evaluation without human bias, researchers leverage pre-trained neural networks:

• Frame- and Segment-Level Classification: Long audio is split into inter-pausal units or sentences and passed through SER models (such as fine-tuned wav2vec 2.0 or HuBERT models). Researchers measure the percentage of segments that retain the target emotional label throughout the entire passage.
• Embedding Distance Trajectories: Instead of relying solely on categorical labels, researchers extract latent emotion embeddings across sequential sentences. By calculating the cosine similarity or Euclidean distance between consecutive segments, they evaluate whether the emotional vector remains stable or degrades toward a neutral centroid.

Acoustic and Prosodic Trajectory Analysis

Emotional expression is grounded in physical speech parameters. Researchers track specific prosodic features over the duration of long passages to detect unnatural fluctuations:

• Fundamental Frequency (\(F_0\)) Contours: Pitch mean, range, and variance are tracked across paragraph segments. Sudden drops in \(F_0\) variance typically indicate that the model is losing expressiveness and defaulting to flat prosody.
• Energy and Speaking Rate: Root Mean Square (RMS) energy profiles and syllable-per-second rates are analyzed to verify that intensity and tempo match the target emotion uniformly across the entire timeline.
• Boundary Transitions: Prosodic parameters are measured specifically at sentence and paragraph boundaries to ensure transitions sound continuous rather than disjointed, which commonly occurs when models synthesize text in independent chunks.

Semantic and Contextual Alignment

Consistency does not always mean maintaining a static emotion; expressive long-form reading often demands subtle shifts dictated by the narrative. Advanced evaluations measure text-audio alignment by comparing the predicted sentiment or emotion of the underlying text script with the extracted emotional profile of the synthesized speech. A successful system maintains the overarching persona while appropriately modulating its delivery to reflect the evolving narrative context.