Publications

An important concern in speaker recognition is the performance degradation that occurs when speaker models trained with speech from one type of channel are subsequently used to score speech from another type of channel, known as channel mismatch. This paper investigates the relative performance of two different spectral subtraction methods...

The estimation of speech parameters and the intelligibility of speech transmitted through low-rate coders, such as MELP, are severely degraded when there are high levels of acoustic noise in the speaking environment. The application of nonacoustic and nontraditional sensors, which are less sensitive to acoustic noise than the standard microphone...

Various psycho-acoustical experiments have concluded that visual features strongly affect the perception of speech. This contribution is most pronounced in noisy environments where the intelligibility of audio-only speech is quickly degraded. An exploration of the effectiveness for extracted visual features such as lip height and width for improving speech intelligibility...

Traditionally speaker recognition techniques have focused on using short-term, low-level acoustic information such as cepstra features extracted over 20-30 ms windows of speech. But speech is a complex behavior conveying more information about the speaker than merely the sounds that are characteristic of his vocal apparatus. This higher-level information includes...

Nonacoustic sensors such as the general electromagnetic motion sensor (GEMS), the physiological microphone (P-mic), and the electroglottograph (EGG) offer multimodal approaches to speech processing and speaker and speech recognition. These sensors provide measurements of functions of the glottal excitation and, more generally, of the vocal tract articulator movements that are...

Many nonacoustic sensors are now available to augment user authentication. Devices such as the GEMS (glottal electromagnetic micro-power sensor), the EGG (electroglottograph), and the P-mic (physiological mic) all have distinct methods of measuring physical processes associated with speech production. A potential exciting aspect of the application of these sensors is...

In this paper, we exploit models of auditory signal processing at different levels along the auditory pathway for use in speaker recognition. A low-level nonlinear model, at the cochlea, provides accentuated signal dynamics, while a a high-level model, at the inferior colliculus, provides frequency analysis of modulation components that reveals...

In this paper, we introduce a new approach to two-dimensional (2-D) processing of the one-dimensional (1-D) speech signal in the time-frequency plane. Specifically, we obtain the shortspace 2-D Fourier transform magnitude of a narrowband spectrogram of the signal and show that this 2-D transformation maps harmonically-related signal components to a...

In this paper we present an approach to close the gap between text-dependent and text-independent speaker verification performance. Text-constrained GMM-UBM systems are created using word segmentations produced by a LVCSR system on conversational speech allowing the system to focus on speaker differences over a constrained set of acoustic units. Results...

In this paper, an adaptive approach to the enhancement of speech signals is developed based on auditory spectral change. The algorithm is motivated by sensitivity of aural biologic systems to signal dynamics, by evidence that noise is aurally masked by rapid changes in a signal, and by analogies to these...