Template-based ASR using Posterior features and synthetic references: comparing different TTS systems
In recent works, the use of phone class-conditional posterior probabilities (posterior features) directly as features provided successful results in template-based ASR systems. In this paper, motivated by the high quality of current text-to-speech systems and the robustness of posterior features toward undesired variability, we investigate the use of synthetic speech to generate reference templates. The use of synthetic speech in template-based ASR not only allows to address the issue of in-domain data collection but also expansion of vocabulary. On 75- and 600-word task-independent and speaker-independent setup of Phonebook corpus, we show the feasibility of this approach by investigating different synthetic voices produced by HTS-based synthesizer trained on two different databases. Our study shows that synthetic speech templates can yield performance comparable to the natural speech templates, especially with synthetic voices that have high intelligibility.