Using linguistic predictability and the lombard effect to increase the intelligibility of synthetic speech in noise

In order to predict which words in a sentence are harder to understand in noise it is necessary to consider not only audibility but also semantic or linguistic information. This paper focuses on using linguistic predictability to inform an intelligibility enhancement method that uses Lombard-adapted synthetic speech to modify low predictable words in Speech Perception in Noise (SPIN) test sentences. Word intelligibility in the presence of speech-shaped noise was measured using plain, Lombard and a combination of the two synthetic voices. The findings show that the Lombard voice increases intelligibility in noise but the intelligibility gap between words in a high and low predictable context still remains. Using a Lombard voice when a word is unpredictable is a good strategy, but if a word is predictable from its context the Lombard benefit only occurs when other words in the sentence are also modified.