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Speakers regulate vocal intensity on many occasions for example to be heard over a long distance or to express vocal emotions. Humans can regulate vocal intensity over a wide sound pressure level (SPL) range and therefore speech can be categorized into different vocal intensity categories. Recent machine learning experiments have studied classification of vocal intensity category from speech signals which have been recorded without SPL information and which are represented on arbitrary amplitude scales. By fine-tuning four pre-trained models (wav2vec2-BASE, wav2vec2-LARGE, HuBERT, audio speech transformers), this paper studies classification of speech into four intensity categories (soft, normal, loud, very loud), when speech is presented on such arbitrary amplitude scale. The fine-tuned model embeddings showed absolute improvements of 5% and 10-12% in accuracy compared to baselines for the target intensity category label and the SPL-based intensity category label, respectively.