Publication
Automatic speech recognition bases its models on the acoustic features derived from the speech signal. Some have investigated replacing or supplementing these features with information that can not be precisely measured (articulator positions, pitch, gender, etc.) automatically. Consequently, automatic estimations of the desired information would be generated. This data can degrade performance due to its imprecisions. In this paper, we describe a system that treats pitch as an auxiliary information within the framework of Bayesian networks, resulting in improved performance.
Romain Essy Théo Gratier De Saint-Louis
Ian Smith, Numa Joy Bertola, Sai Ganesh Sarvotham Pai
Laurent Valentin Jospin, Jesse Ray Murray Lahaye