The speech rate in Text-to-Speech (TTS) systems directly affects intelligibility by influencing how clearly listeners perceive words and phrases. At normal speaking rates (around 150-160 words per minute), TTS output typically maintains a balance between naturalness and clarity. However, deviations from this range—either faster or slower—can degrade intelligibility. Faster rates compress phonetic segments, reducing pauses between words and altering prosody, which makes it harder to distinguish sounds or parse sentence structure. Slower rates stretch phonemes unnaturally, disrupting rhythm and causing listeners to lose track of context. Intelligibility often follows a U-shaped curve: it peaks near natural speech rates and declines as the rate moves too far from this range.
For example, accelerating TTS output beyond 180 words per minute might cause phonemes like /b/ and /p/ (distinguished by voice onset time) to blur, leading to confusion between words like "bat" and "pat." Similarly, compressing syllables in a phrase like "I scream" could make it sound like "ice cream." Conversely, slowing speech below 120 words per minute might break the flow of a sentence like "The quick brown fox jumps," turning it into disjointed segments ("The... quick... brown..."), which strains working memory. In multilingual contexts, non-native speakers or those with hearing impairments may struggle even more with non-optimal rates, as they rely heavily on clear articulation and pacing to decode unfamiliar sounds.
Technically, TTS systems handle rate adjustments through duration modeling. Concatenative systems stretch or shrink pre-recorded speech units, risking artifacts like robotic tones or choppy transitions. Neural models, like Tacotron or FastSpeech, adjust duration predictors at the phoneme level, allowing smoother scaling while preserving prosodic features. However, over-scaling can still distort formants (vowel characteristics) or suppress emphasis on stressed syllables. Developers can mitigate these issues by implementing rate controls that limit adjustments to a safe range (e.g., ±30% of default speed) or applying signal processing techniques like PSOLA (Pitch Synchronous Overlap and Add) to modify rate without altering pitch. Testing with user groups is critical to identify optimal rate settings for specific use cases, such as audiobooks versus navigation prompts.