Abstract
In this paper, a manifold subspace learning algorithm based on locality preserving discriminant projection (LPDP) is used for speaker verification. LPDP can overcome the deficiency of the total variability factor analysis and locality preserving projection (LPP). LPDP can effectively use the speaker label information of speech data. Through optimization, LPDP can maintain the inherent manifold local structure of the speech data samples of the same speaker by reducing the distance between them. At the same time, LPDP can enhance the discriminability of the embedding space by expanding the distance between the speech data samples of different speakers. The proposed method is compared with LPP and total variability factor analysis on the NIST SRE 2010 telephone-telephone core condition. The experimental results indicate that the proposed LPDP can overcome the deficiency of LPP and total variability factor analysis and can further improve the system performance.
Highlights
Speaker verification is a subtask of speaker recognition, whose purpose is to verify whether a segment of speech is spoken by a designated speaker [1] [2]
To compensate for the deficiency, we introduced locality preserving projection (LPP) [11], neighborhood preserving embedding (NPE) [12], and discriminant neighborhood embedding (DNE) [13] to speaker verification
To verify the performance of the proposed locality preserving discriminant projection (LPDP) algorithm, we experimentally compared it with the traditional total variability factor analysis and LPP algorithms
Summary
Speaker verification is a subtask of speaker recognition, whose purpose is to verify whether a segment of speech is spoken by a designated speaker [1] [2]. Total variability factor analysis has been widely used in speaker verification [3] [4] [5] [6]. LPP is an unsupervised learning algorithm [11] [16] that is not concerned with the speaker label information in the dimensionality-reduction process and does not make use of the discriminative information between the speech data of different speakers. In view of the above shortcomings of LPP, we apply the locality preserving discriminant projection (LPDP) algorithm in speaker verification. LPDP can bring in the speaker label information from the speech data and, through optimization, preserve the inherent local manifold structure of the speech data samples from the same speaker to reduce the distance between them.
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