Individually amplified text-to-speech

Text-to-speech (TTS) offers the opportunity to compensate for a hearing loss at the source rather than correcting for it at the receiving end. This removes limitations such as time constraints for algorithms that amplify a sound individually and can lead to higher speech quality for hearing-impaired listeners. We propose an algorithm that restores loudness to normal perception at a high resolution in time, frequency and level, and embed it in a TTS system that uses Tacotron2 and WaveGlow to produce individually amplified speech. Subjective evaluations of speech quality showed that the proposed algorithm led to high-quality audio. Mean opinion scores were predicted well by the STOI metric. Transfer learning led to a quick adaption of the produced spectra from original speech to individually amplified speech and gives us a way to train an individual TTS system efficiently.