Valence and arousal in speech emotion AI

Valence and arousal describe two different aspects of an emotional expression. Valence runs from negative to positive; arousal runs from calm to activated. A speech model trained for dimensional emotion recognition estimates these coordinates from audio. A categorical model instead returns labels such as happy, sad, angry, or worried. Neither output directly establishes what a speaker privately feels.

The distinction matters even for everyday recordings. Excited and angry speech can both sound highly activated, while differing in valence. Relaxed satisfaction and excitement can both be positive while differing in arousal. These are illustrative patterns, not rules for diagnosing a recording: the speaker, language, conversational context, and annotation protocol all affect the interpretation.

Text sentiment is a third task. It concerns the evaluation expressed by words, which can disagree with vocal delivery. A positive sentence can be spoken sarcastically or with frustration. If a product needs that distinction, preserve the audio and keep transcript sentiment separate from acoustic-expression scores. Do not rename either one “valence” without specifying the target and validating the mapping.

Choose the output your application actually needs. For continuous trajectories, select a model that explicitly exposes dimensional predictions and document its scale, time resolution, and training labels. audEERING’s published wav2vec 2.0 model is one example with arousal, dominance, and valence outputs. Its model card specifies approximately 0–1 outputs and a noncommercial license; inspect the current license before choosing it for a product. A model’s language coverage and performance must also match your recordings.

Oruk’s file emotion API serves a different contract: it selects scores from a 15-label emotion vocabulary, with time-local segments for longer recordings. The unified analysis endpoint also returns the transcript and speaking style. These outputs can support recording review, but there are no native continuous valence or arousal fields in this contract. The separate Realtime preview exposes phrase emotion scores alongside live transcription. Select a dimensional model if your integration specifically requires dimensional coordinates.

Converting categories to polarity can be useful for a declared evaluation, but it is a new task with a new denominator. Our historical IEMOCAP comparison groups angry, frustrated, and sad into negative; neutral into neutral; and happy and excited into positive. That three-class benchmark is not continuous valence regression or an arousal evaluation. Its systems also have different valid-response counts. The methodology and per-system coverage belong alongside the scores, and a historical result must not be presented as a fresh test of the production API.

Averaging the scores of positive labels and subtracting negative ones does not automatically produce calibrated valence. Oruk selects labels using model-specific thresholds and a highest-scoring fallback when no emotion passes. Omitted labels are not a complete set of zero-valued probabilities, and the returned scores need not sum to one. Such a transform would be a custom heuristic that needs its own labels and validation, not an undocumented API capability.

Evaluate dimensional predictions against continuous human ratings, reporting the scale and agreement metric, such as concordance correlation coefficient, together with error and coverage. For categorical outputs, report per-class precision and recall, macro F1, and the decision thresholds. Keep speakers and sessions separate across fitting and evaluation, include failures and abstentions in the report, and test representative microphones, languages, and conversational conditions. A three-class accuracy number cannot be compared directly with a regression correlation.