Abstract
In this paper, a new discriminative likelihood score weighting technique is proposed for speaker identification. The proposed method employs a discriminative weighting of frame-level log-likelihood scores with acoustic-phonetic classification in the Gaussian mixture model (GMM)-based speaker identification. Experiments performed on the Aurora noise-corrupted TIMIT database showed that the proposed approach provides meaningful performance improvement with an overall relative error reduction of 15.8% over the maximum likelihood-based baseline GMM approach.
Highlights
Speaker recognition mainly consists of two different tasks, speaker identification and speaker verification
To test the proposed technique in the telephone-based various noisy environments, the original 16-kHz sampled clean speech data have been downsampled to 8-kHz sampling rate. These clean speech data are artificially added with four kinds of the Aurora noise [18] composed of car, restaurant, subway, and street, with three signal-to-noise ratio (SNR) levels of 20, 10, and 0 dB, which results in a set of noisy speech data consisting of 12 noisy conditions, each with 2,000 utterances
The baseline speaker identification system was built from the Gaussian mixture modeluniversal background model (GMM-UBM) by using the maximum a posteriori (MAP)-based adapted GMM
Summary
Speaker recognition mainly consists of two different tasks, speaker identification and speaker verification. Conventional GMM-based speaker identification approaches extract registered speakers’ information just by the weighted sum of their corresponding frame-level scores. We expect that the performance of speaker identification can be improved by applying discriminative weights on speech frames after taking into full account their acoustic-phonetic classes as well as speaker’s voice characteristics in deriving speaker scores.
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