  Recent trends in neural network based text-to-speech/speech synthesis
pipelines have employed recurrent Seq2seq architectures that can synthesize
realistic sounding speech directly from text characters. These systems however
have complex architectures and takes a substantial amount of time to train. We
introduce several modifications to these Seq2seq architectures that allow for
faster training time, and also allows us to reduce the complexity of the model
architecture at the same time. We show that our proposed model can achieve
attention alignment much faster than previous architectures and that good audio
quality can be achieved with a model that's much smaller in size. Sample audio
available at https://soundcloud.com/gary-wang-23/sets/tts-samples-for-cmpt-419.
