The current method for phonetic landmark detection in the Spoken Language Systems Group at MIT is performed by SUMMIT, a segment-based speech recognition system. Under noisy conditions the system's segmentation algorithm has difficulty distinguishing between noise and speech components and often produces a poor alignment of sounds. Noise robustness in SUMMIT can be improved using a full segmentation method, which allows landmarks at regularly spaced intervals. While this approach is computationally more expensive than the original segmentation method, it is more robust under noisy environments. In this thesis, we explore a landmark detection and segmentation algorithm using the McAulay-Quatieri Sinusoidal Model, in hopes of improving the performance of the recognizer in noisy conditions. We first discuss the sinusoidal model representation, in which rapid changes in spectral components are tracked using the concept of "birth" and "death" of underlying sinewaves. Next, we describe our method of landmark detection with respect to the behavior of sinewave tracks generated from this model. These landmarks are interconnected together to form a graph of hypothetical segments.
展开▼
