7.1 Conclusion
In this thesis, we have discussed the assumption of low-dimensionality of channel space typical in current state-of-the art speaker verification systems. We have proposed to partition the acoustic space into local regions where we can apply channel compensation techniques separately. This would lead to more tractable solutions and gives a better view of the channel and speaker spaces. We argued that obtaining a single number as the distance between two speakers is questionable and using local regions may also support better modeling of inter-speaker variabilities. We have proposed two architectures for speaker verification in local regions in which current approaches can easily be used.
The two architectures both apply channel compensation locally and differ in where the subsequent speaker modeling and scoring steps are realized. We have implemented a GMM supervector SVM system with local NAP based on one of these architectures.
We have also worked on a special case of intra-speaker variability, namely within-session variability. We have proposed a method to improve performance by compensating vari-abilities mainly coming from actual realizations of phones as well as varivari-abilities across phones. We have shown that our technique not only improves performance for short test utterances but also for long test utterances.
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Chapter 7. Conclusion and Future Work 61
7.2 Future Work
For future improvements on our proposals, we will focus on the below directions.
• Further studies on local region based speaker verification must be explored. We want to apply other approaches like TVS, LDA + WCCN, and JFA in the local architectures we proposed. In this thesis we only used one of the architectures (Architecture 1) where only channel compensation is applied locally. It may be interesting to model speakers and inter-speaker variabilities locally. We also want to explore how well local systems may be fused in the score level. Since the dimension of local regions are small enough we can leave the linearity assumption and try to learn the manifold of speaker space.
• Better ways of partitioning the acoustic space is also worth working on. It will be most beneficial if we can find a way to partition the space that will help in subsequent steps of channel compensation.
• In this thesis we have used random sampling to generate short segments from the training utterance. We tried to learn a better hyperplane by SVM taking phonetic intra-speaker variabilities into account. As a future work, we plan to investigate ways to remove these variabilities by feature transforms applied to the training supervectors.
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