To adjust intonation and stress for natural speech, focus on mimicking the rhythmic and melodic patterns of human language. Intonation refers to the rise and fall of pitch across phrases, while stress involves emphasizing specific syllables or words. For example, in English, declarative sentences typically end with a falling pitch, while questions often rise. Stress is applied to content words (nouns, verbs) more than function words (prepositions, articles) to convey meaning. Tools like speech synthesis markup language (SSML) let developers program pitch changes and word emphasis, while machine learning models trained on human speech datasets learn to predict these patterns automatically. Naturalness improves when variations align with the speaker’s intent, such as raising pitch for excitement or elongating stressed syllables.
To implement stress, adjust duration, volume, and pitch on key syllables. For instance, the word "record" (noun) stresses the first syllable (RE-cord), while "record" (verb) stresses the second (re-CORD). Algorithms can identify stress patterns using linguistic rules (like syllable counting) or neural networks trained on phonetic data. In code, this might involve modifying the prosody parameters in a text-to-speech engine or preprocessing text to tag stressed words. For intonation, map pitch contours to sentence types: a steady decline for statements, a sharp rise for questions, or a plateau for lists. Tools like Praat or Python’s librosa can analyze and replicate these patterns programmatically.
Practical adjustments require testing with real-world examples. For instance, the sentence "I didn’t say he stole the money" changes meaning based on which word is stressed. Developers can use SSML tags like <prosody> to manually set pitch and rate or integrate pretrained models like Tacotron 2 that generate intonation from context. Balancing automation with rule-based tweaks (e.g., emphasizing proper nouns) ensures flexibility. The goal is to avoid monotony by introducing variability while adhering to the grammatical and emotional context of the speech.