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Acoustic model training using feature vectors generated by manipulating speech parameters of real speakers | IEEE Conference Publication | IEEE Xplore

Acoustic model training using feature vectors generated by manipulating speech parameters of real speakers


Abstract:

In this paper, we propose a robust speaker-independent acoustic model training method using generative training to generate many pseudo-speakers from a small number of re...Show More

Abstract:

In this paper, we propose a robust speaker-independent acoustic model training method using generative training to generate many pseudo-speakers from a small number of real speakers. We focus on the difference between each speaker's vocal tract length, and manipulate it in order to create many different pseudo-speakers with a range of vocal tract lengths. This method employs frequency warping based on the inverted use Vocal Tract Length Normalization(VTLN). Another method for creating pseudo-speakers is to vary the speaking rate of the speakers. This can be achieved by a method called PICOLA; Pointer Interval Controlled OverLap and Add. In experiments, we train acoustic models using these generated pseudo-speakers in addition to the original speakers. Evaluation results show that generating pseudo-speakers by manipulating speaking rates did not result in a sufficient increase in performance, however, vocal tract length warping was effective.
Date of Conference: 03-06 December 2012
Date Added to IEEE Xplore: 17 January 2013
ISBN Information:
Conference Location: Hollywood, CA, USA

References

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