Lombard modified text-to-speech synthesis for improved intelligibility: submission for the hurricane challenge 2013

This paper describes modification of a TTS system for improving the intelligibility of speech in various noise conditions. First, the GlottHMM vocoder is used for training a voice with modal speech data. The vocoder and voice parameters are then modified to mimic the properties of Lombard effect based on a small amount of Lombard speech from the same speaker. More specifically, the durations are increased, fundamental frequency is raised, spectral tilt is decreased, the harmonic-to-noise ratio is increased, and a pressed glottal flow pulses are used in creating excitation. The formants of the speech are also enhanced and finally the speech is compressed in order to increase noise robustness of the voice. The evaluation results of the Hurricane Challenge 2013 indicate that the modified voice is mostly less intelligible than the unmodified natural speech, as expected, but more intelligible than the reference TTS voice, especially in the low SNR conditions.