Synthesis errors in text-to-speech (TTS) systems directly degrade the perceived quality of output by introducing inconsistencies that listeners notice as unnatural or distracting. These errors occur when the system fails to accurately model human speech patterns, resulting in mispronunciations, awkward pauses, incorrect intonation, or artifacts like robotic buzzing. For example, a TTS system might misplace stress in a word (e.g., “REcord” vs. “reCORD”), making the sentence sound unnatural. Listeners perceive these errors as markers of low quality, even if the rest of the output is smooth, because human ears are highly sensitive to deviations from natural speech rhythms and sounds.
Specific types of errors impact different aspects of quality. Pronunciation mistakes, such as mangling uncommon names or technical terms (e.g., “Cholmondeley” pronounced phonetically instead of “Chumley”), break immersion and reduce clarity. Prosody errors—like flat intonation in questions or misplaced pauses—can make speech sound monotonic or emotionally disconnected, undermining engagement. Artifacts, such as glitches or background noise, are particularly jarring and often signal technical limitations. For instance, a concatenative TTS system might splice audio units poorly, creating audible clicks, while a neural model might generate “buzzy” vowels due to overfitting. Contextual errors also matter: a TTS system that emphasizes the wrong word in “I didn’t steal the money” changes the sentence’s meaning, confusing listeners.
Developers can mitigate these issues by refining core TTS components. Improving grapheme-to-phoneme models reduces mispronunciations, while better prosody prediction algorithms (e.g., using linguistic features or neural predictors) ensure natural rhythm and emphasis. Artifacts are minimized through higher-quality training data, noise reduction, or post-processing filters. Testing with diverse datasets—including rare words, dialects, and emotional speech—helps uncover edge cases. However, balancing quality and computational efficiency remains a challenge. For example, real-time systems might prioritize lighter models, accepting minor trade-offs in naturalness. Perceptual evaluation metrics (e.g., MOS scores) and user feedback are critical for identifying errors that automated metrics miss. Ultimately, reducing synthesis errors requires iterative tuning of both the model architecture and the pipeline’s preprocessing/postprocessing steps.