Markus Becker
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Frugal Paradigm Completion
Alex Erdmann
Christian Schallhart
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020), pp. 8248-8273
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Lexica distinguishing all morphologically related forms of each lexeme are crucial to many language technologies, yet building them is expensive. We propose Frugal Paradigm Completion, an approach that predicts all related forms in a morphological paradigm from as few manually provided forms as possible. It induces typological information during training which it uses to determine the best sources at test time. We evaluate our language-agnostic approach on 7 diverse languages. Compared to popular alternative approaches, our Frugal Paradigm Completion approach reduces manual labor by 16-63% and
is the most robust to typological variation.
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Recent Advances in Google Real-time HMM-driven Unit Selection Synthesizer
Siamak Tazari
Hanna Silen
International Speech Communication Association (ISCA), Sep 8--12, San Francisco, USA, pp. 2238-2242
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This paper presents advances in Google's hidden Markov model (HMM)-driven unit selection speech synthesis system. We describe several improvements to the run-time system; these include minimal
latency, high-quality and fast refresh cycle for new voices. Traditionally unit selection synthesizers are limited in terms of the amount of data they can handle and the real applications they
are built for. That is even more critical for real-life large-scale applications where high-quality is expected and low latency is required given the available computational resources. In this paper we present an optimized engine to handle a large database at runtime, a composite unit search approach for combining diphones and phrase-based units. In addition a new voice building strategy for handling big
databases and keeping the building times low is presented.
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