Measuring the effect of reverberation on statistical parametric speech synthesis

Abstract

Text-to-speech (TTS) synthesis is the technique of generating intelligible speech from a given text. The most recent techniques for TTS are based on machine learning, implementing systems which learn linguistic specifications and their corresponding parameters of the speech signal. Given the growing interest in implementing verbal communication systems in different devices, such as cell phones, car navigation system and personal assistants, it is important to use speech data from many sources. The speech recordings available for this purpose are not always generated with the best quality. For example, if an artificial voice is created from historical recordings, or a voice created from a person whom only a small set of recordings exists. In these cases, there is an additional challenge due to the adverse conditions in the data. Reverberation is one of the conditions that can be found in these cases, a product of the different trajectories that a speech signal can take in an environment before registering through a microphone. In the present work, we quantitatively explore the effect of different levels of reverberation on the quality of artificial voice generated with those references. The results show that the quality of the generated artificial speech is affected considerably with any level of reverberation. Thus, the application of algorithms for speech enhancement must be taken always into consideration before and after any process of TTS.