Technical Program

Paper Detail

Paper:	SLP-P1.6
Session:	Feature Extraction and Modeling
Time:	Tuesday, May 16, 10:30 - 12:30
Presentation:	Poster
Topic:	Speech and Spoken Language Processing: Feature Extraction and Modeling
Title:	AUTOMATIC SPEECH ATTRIBUTE TRANSCRIPTION (ASAT) – THE FRONT END PROCESSOR
Authors:	Jun Hou, Lawrence Rabiner, Sorin Dusan, Rutgers University, United States
Abstract:	In this paper we discuss the design and implementation of the ASAT front end processing system, whose goal is to convert the speech waveform into a range of measurements and parameters which are then combined to form probabilistic attributes. The ASAT front end processing module utilizes a range of spectral and temporal speech parameters as input to a set of neural network classifiers to create sets of attribute probability lattices, based on either single frames or blocks of frames (segments). We test this architecture by using the 14 Sound Patterns of English (SPE) features as speech attributes. Without balancing the training data, the detection accuracies of 4 of the SPE features are above 90%, 2 features obtain between 80% and 90% detection accuracy, and 8 features have detection accuracies below 80%. With a novel method of balancing the feature training data, the performance of the neural networks improved significantly, with 6 features having detection accuracies above 90% and the remaining 8 features with detection accuracy above 80%.