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Speaker recognition from an unknown utterance and speaker-speech interaction

R.L. Kashyap
- Vol. 77, pp 15279
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TLDR
In this paper, the authors developed various types of tests for speaker verification and identification using only one phoneme segment or the entire utterance using statistical decision theory, and considered the role of speaker variability in speech recognition and recognize its complementarity to the problem of optimal choice of phonemes for speaker recognition.
Abstract
We are interested in determining whether the given utterance comes from a member of a given speaker group or an imposter. If it is the former, we are interested in determining the identity of the speaker. The only knowledge available is a set of known utterances from the given group of speakers. The given utterance is manually divided into phonemes without necessarily ascertaining the identity of phonemes. Using statistical decision theory, we will develop various types of tests for speaker verification and identification using only one phoneme segment or the entire utterance. We will consider related problems such as the methods of clustering speakers to aid speaker verification, the optimal choice of phonemes for speaker recognition. Next we consider the role of speaker variability in speech recognition and recognize its complementarity to the problem of optimal choice of phonemes for speaker recognition. We illustrate the efficacy of the various methods developed here by considering the speaker and speech identification problems with three speech data bases.

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Citations
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Optimal feature selection and decision rules in classification problems with time series

TL;DR: The optimal feature set and the corresponding optimal decision rule are compared with other feature sets and decision rules mentioned in the literature on speech recognition.
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Automatic speaker identification for a large population

TL;DR: Analysis and design of a two-stage pattern classifier for speaker identification in a population of 30 is considered and a subset of the total feature set is given that gives an absolute identification of the speaker's identity.
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Phonological content impact on wrongful convictions in Forensic Voice Comparison context

TL;DR: It is shown that even though the vast majority of speaker pairs are well discriminated, few pairs are difficult to distinguish and all the phonemic content play a positive role in speaker discrimination while for the “worst” pairs, it appears that nasals have a negative effect and lead to a confusion between speakers.
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Talker recognition in tandem with talker-independent isolated word recognition

TL;DR: A talker recognition system operating in tandem with a talker-independent isolated word recognizer is described and evaluated, and it is shown that goodtalker recognition performance can be obtained for input utterances consisting of sequences of seven or more digits.
References
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Journal ArticleDOI

Effectiveness of linear prediction characteristics of the speech wave for automatic speaker identification and verification

TL;DR: The cepstrum was found to be the most effective, providing an identification accuracy of 70% for speech 50 msec in duration, which increased to more than 98% for a duration of 0.5 sec.
Journal ArticleDOI

Selection of acoustic features for speaker identification

TL;DR: In this paper, a set of acoustic features in the speech signal that are effective for the identification of a speaker was determined, and the analysis technique of linear prediction was incorporated to examine features that were previously ignored because their measurement was either too time consuming or not easily amenable to automatic measurement.
Journal ArticleDOI

New techniques for automatic speaker verification

TL;DR: An interactive automatic speaker verification system has been augmented to include linear prediction parameters in addition to the already existing pitch and intensity analysis of sentence-long utterances which significantly improves its performance.
Journal ArticleDOI

Speaker Identification Based on Nasal Phonation

TL;DR: A method of automatic speaker identification based on the physiology of the vocal apparatus and essentially independent of the spoken message has been developed.
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