Abstract not found

defined), by Ben Gold and Nelson Morgan.

This paper gives an overview of automatic speaker recognition technology, with an emphasis on text-independent recognition. Speaker recognition has been studied actively for several decades. We give an overview of both the classical and the state-of-the-art methods. We start with the fundamentals of automatic speaker recognition, concerning feature extraction and speaker modeling. We elaborate advanced computational techniques to address robustness and session variability. The recent progress from vectors towards supervectors opens up a new area of exploration and represents a technology trend. We also provide an overview of this recent development and discuss the evaluation methodology of speaker recognition systems. We conclude the paper with discussion on future directions.

In practical applications of pattern recognition, there are often different features extracted from raw data which needs recognizing. Methods of combining multiple classifiers with different features are viewed as a general problem in various application areas of pattern recognition. In this paper, a systematic investigation has been made and possible solutions are classified into three frameworks, i.e. linear opinion pools, winnertake -all and evidential reasoning. For combining multiple classifiers with different features, a novel method is presented in the framework of linear opinion pools and a modified training algorithm for associative switch is also proposed in the framework of winner-take-all. In the framework of evidential reasoning, several typical methods are briefly reviewed for use. All aforementioned methods have already been applied to text-independent speaker identification. The simulations show that results yielded by the methods described in this paper are better than...

Identity verification systems are an important part of our every day life. A typical example is the Automatic Teller Machine (ATM) which employs a simple identity verification scheme: the user is asked to enter their secret password after inserting their ATM card; if the password matches the one prescribed to the card, the user is allowed access to their bank account. This scheme suffers from a major drawback: only the validity of the combination of a certain possession (the ATM card) and certain knowledge (the password) is verified. The ATM card can be lost or stolen, and the password can be compromised. Thus new verification methods have emerged, where the password has either been replaced by, or used in addition to, biometrics such as the person's speech, face image or fingerprints. Apart from the ATM example described above, biometrics can be applied to other areas, such as telephone & internet based banking, airline reservations & check-in, as well as forensic work and law enforcement applications. Biometric systems

Abstract. Higher-level features based on linguistic or long-range information have attracted significant attention in automatic speaker recognition. This article briefly summarizes approaches to using higher-level features for text-independent speaker verification over the last decade. To clarify how each approach uses higher-level information, features are described in terms of their type, temporal span, and reliance on automatic speech recognition for both feature extraction and feature conditioning. A subsequent analysis of higher-level features in a state-of-the-art system illustrates that (1) a higher-level cepstral system outperforms standard systems, (2) a prosodic system shows excellent performance individually and in combination, (3) other higher-level systems provide further gains, and (4) higher-level systems provide increasing relative gains as training data increases. Implications for the general field of speaker classification are discussed.

While there has been a long tradition of research seeking to use prosodic features, especially pitch, in speaker recognition systems, results have generally been disappointing when such features are used in isolation and only modest improvements have been seen when used in conjunction with traditional cepstral GMM systems. In contrast, we report here on work from the JHU 2002 Summer Workshop exploring a range of prosodic features, using as testbed NIST’s 2001 Extended Data task. We examined a variety of modeling techniques, such as n-gram models of turn-level prosodic features and simple vectors of summary statistics per conversation side scored by k th nearestneighbor classifiers. We found that purely prosodic models were able to achieve equal error rates of under 10%, and yielded significant gains when combined with more traditional systems. We also report on exploratory work on “conversational” features, capturing properties of the interaction across conversation sides, such as turn-taking patterns. 1.

We investigate the incorporation of larger time-scale information, such as prosody, into standard speaker ID systems. Our study is based on the Extended Data Task of the NIST 2001 Speaker ID evaluation, which provides much more test and training data than has traditionally been available to similar speaker ID investigations. In addition, we have had access to a detailed prosodic feature database of Switchboard-I conversations, including data not previously applied to speaker ID. We describe two baseline acoustic systems, an approach using Gaussian Mixture Models, and an LVCSR-based speaker ID system. These results are compared to and combined with two larger time-scale systems: a system based on an "idiolect" language model, and a system making use of the contents of the prosody database. We find that, with sufficient test and training data, suprasegmental information can significantly enhance the performance of traditional speaker ID systems.

Automatic speech recognition bases its models on the acoustic features derived from the speech signal. Some have investigated replacing or supplementing these features with information that can not be precisely measured (articulator positions, pitch, gender, etc.) automatically. Consequently, automatic estimations of the desired information would be generated. This data can degrade performance due to its imprecisions. In this paper, we describe a system that treats pitch as an auxiliary information within the framework of Bayesian networks, resulting in improved performance. 1.

Front-end or feature extractor is the first component in an automatic speaker recognition system. Feature extraction transforms the raw speech signal into a compact but e#ective representation that is more stable and discriminative than the original signal. Since the front-end is the first component in the chain, the quality of the later components (speaker modeling and pattern matching) is strongly determined by the quality of the front-end. In other words, classification can be at most as accurate as the features.