Work

Phonetic variability constrained bottleneck features for joint speaker recognition and physical task stress detection

Year: 2020

Type: article

Abstract: Normalizing intrinsic variabilities (e.g., variability in speech production brought on by aging, physical or cognitive task stress, Lombard effect, etc.) in speech and speaker recognition models is essential for system robustness. This study focuses on analysis of speech under physical task stress with its application for speaker recognition and physical task stress detection. An innovative framework using deep neural networks (DNNs) for joint text-independent speaker recognition and physical task stress detection is proposed. Instead of processing consecutive feature frames like d-vectors (i.e., average of frame-level speaker specific features extracted with a speaker discriminative trained DNN), phonetic variability constrained feature vectors as inputs to train deep bottleneck neural networks is proposed. More specifically, a universal background model (UBM) with a small number of mixtures is employed to align the acoustic features. The innovative feature representation is then generated by selecting and concatenating frames according to the alignments from the UBM. The benefit of feature rearrangement is twofold. First, phonetic variability is largely constrained in the front-end feature vector. Second, by sampling a determined number of representations for each speaker/utterance, the issue of data imbalance and over-fitting is alleviated. Experiments for speaker recognition and physical stress detection are conducted on the UTScope-Physical Task Stress Corpus. Improved performance in terms of accuracy (for identification/detection task) and Equal Error Rate (for verification task) over a strong i-vector probabilistic linear discriminant analysis system confirms the effectiveness of this proposed method.

Source: The Journal of the Acoustical Society of America

DOI: 10.1121/10.0002455

Authors: Chunlei Zhang, John H. L. Hansen

Institution: The University of Texas at Dallas

Language: English

Topic: Speech Recognition and Synthesis

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: National Science Foundation, Air Force Research Laboratory

Awards: Collaborative Research: CSL-MultiAD: Assessing Collaborative STEM Learning through Rich Information Flow based on Multi-Sensor Audio Diarization, G5339030464