  Despite advances in deep learning, current state-of-the-art speech emotion
recognition (SER) systems still have poor performance due to a lack of speech
emotion datasets. This paper proposes augmenting SER systems with synthetic
emotional speech generated by an end-to-end text-to-speech (TTS) system based
on an extended Tacotron architecture. The proposed TTS system includes encoders
for speaker and emotion embeddings, a sequence-to-sequence text generator for
creating Mel-spectrograms, and a WaveRNN to generate audio from the
Mel-spectrograms. Extensive experiments show that the quality of the generated
emotional speech can significantly improve SER performance on multiple
datasets, as demonstrated by a higher mean opinion score (MOS) compared to the
baseline. The generated samples were also effective at augmenting SER
performance.
