Publications

Estimating and evaluating confidence has become a key aspect of the speaker recognition problem because of the increased use of this technology in forensic applications. We discuss evaluation measures for speaker recognition and some of their properties. We then propose a framework for confidence estimation based upon scores and metainformation...

One of the major remaining challenges to improving accuracy in state-of-the-art speaker recognition algorithms is reducing the impact of channel and handset variations on system performance. For Gaussian Mixture Model based speaker recognition systems, a variety of channel-adaptation techniques are known and available for adapting models between different channel conditions...

Discriminatively trained support vector machines have recently been introduced as a novel approach to speaker recognition. Support vector machines (SVMs) have a distinctly different modeling strategy in the speaker recognition problem. The standard Gaussian mixture model (GMM) approach focuses on modeling the probability density of the speaker and the background...

Support vector machines (SVMs) have become a popular tool for discriminative classification. Powerful theoretical and computational tools for support vector machines have enabled significant improvements in pattern classification in several areas. An exciting area of recent application of support vector machines is in speech processing. A key aspect of applying...

Recently, high-level features such as word idiolect, pronunciation, phone usage, prosody, etc., have been successfully used in speaker verification. The benefit of these features was demonstrated in the NIST extended data task for speaker verification; with enough conversational data, a recognition system can become familiar with a speaker and achieve...

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...

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...

Software-defined radios and cognitive radios offer tremendous promise, while having great need for user authentication. Authenticating users is essential to ensuring authorized access and actions in private and secure communications networks. User authentication for software-defined radios and cognitive radios is our focus here. We present various means of authenticating users...