A Machine Speech Chain Approach for Dynamically Adaptive Lombard TTS in Static and Dynamic Noise Environments

Recent end-to-end text-to-speech synthesis (TTS) systems have successfully synthesized high-quality speech. However, TTS speech intelligibility degrades in noisy environments because most of these systems were not designed to handle noisy environments. Several works attempted to address this problem by using offline fine-tuning to adapt their TTS to noisy conditions. Unlike machines, humans never perform offline fine-tuning. Instead, they speak with the Lombard effect in noisy places, where they dynamically adjust their vocal effort to improve the audibility of their speech. This ability is supported by the speech chain mechanism, which involves auditory feedback passing from speech perception to speech production. This paper proposes an alternative approach to TTS in noisy environments that is closer to the human Lombard effect. Specifically, we implement Lombard TTS in a machine speech chain framework to synthesize speech with dynamic adaptation. Our TTS performs adaptation by generating speech utterances based on the auditory feedback that consists of the automatic speech recognition (ASR) loss as the speech intelligibility measure and the speech-to-noise ratio (SNR) prediction as power measurement. Two versions of TTS are investigated: non-incremental TTS with utterance-level feedback and incremental TTS (ITTS) with short-term feedback to reduce the delay without significant performance loss. Furthermore, we evaluate the TTS systems in both static and dynamic noise conditions. Our experimental results show that auditory feedback enhanced the TTS speech intelligibility in noise.