Improving the quality of neural TTS using long-form content and multi-speaker multi-style modeling

Neural text-to-speech (TTS) can provide quality close to natural speech if an adequate amount of high-quality speech material is available for training. However, acquiring speech data for TTS training is costly and time-consuming, especially if the goal is to generate different speaking styles. In this work, we show that we can transfer speaking style across speakers and improve the quality of synthetic speech by training a multi-speaker multi-style (MSMS) model with long-form recordings, in addition to regular TTS recordings. In particular, we show that 1) multi-speaker modeling improves the overall TTS quality, 2) the proposed MSMS approach outperforms pre-training and fine-tuning approach when utilizing additional multi-speaker data, and 3) long-form speaking style is highly rated regardless of the target text domain.

Vocal effort modeling in neural TTS for improving the intelligibility of synthetic speech in noise

We present a neural text-to-speech (TTS) method that models natural vocal effort variation to improve the intelligibility of synthetic speech in the presence of noise. The method consists of first measuring the spectral tilt of unlabeled conventional speech data, and then conditioning a neural TTS model with normalized spectral tilt among other prosodic factors. Changing the spectral tilt parameter and keeping other prosodic factors unchanged enables effective vocal effort control at synthesis time independent of other prosodic factors. By extrapolation of the spectral tilt values beyond what has been seen in the original data, we can generate speech with high vocal effort levels, thus improving the intelligibility of speech in the presence of masking noise. We evaluate the intelligibility and quality of normal speech and speech with increased vocal effort in the presence of various masking noise conditions, and compare these to well-known speech intelligibility-enhancing algorithms. The evaluations show that the proposed method can improve the intelligibility of synthetic speech with little loss in speech quality.