  The increased adoption of digital assistants makes text-to-speech (TTS)
synthesis systems an indispensable feature of modern mobile devices. It is
hence desirable to build a system capable of generating highly intelligible
speech in the presence of noise. Past studies have investigated style
conversion in TTS synthesis, yet degraded synthesized quality often leads to
worse intelligibility. To overcome such limitations, we proposed a novel
transfer learning approach using Tacotron and WaveRNN based TTS synthesis. The
proposed speech system exploits two modification strategies: (a) Lombard
speaking style data and (b) Spectral Shaping and Dynamic Range Compression
(SSDRC) which has been shown to provide high intelligibility gains by
redistributing the signal energy on the time-frequency domain. We refer to this
extension as Lombard-SSDRC TTS system. Intelligibility enhancement as
quantified by the Intelligibility in Bits (SIIB-Gauss) measure shows that the
proposed Lombard-SSDRC TTS system shows significant relative improvement
between 110% and 130% in speech-shaped noise (SSN), and 47% to 140% in
competing-speaker noise (CSN) against the state-of-the-art TTS approach.
Additional subjective evaluation shows that Lombard-SSDRC TTS successfully
increases the speech intelligibility with relative improvement of 455% for SSN
and 104% for CSN in median keyword correction rate compared to the baseline TTS
method.
