Transfer tasks in text-to-speech (TTS) synthesis -where one or more aspects of the speech of one set of speakers is transferred to another set of speakers that do not feature these aspects originally -remains a challenging task.One of the challenges is that models that have high-quality transfer capabilities can have issues in stability, making them impractical for user-facing critical tasks.This paper demonstrates that transfer can be obtained by training a robust TTS system on data generated by a less robust TTS system designed for a high-quality transfer task; in particular, a CHiVE-BERT monolingual TTS system is trained on the output of a Tacotron model designed for accent transfer.While some quality loss is inevitable with this approach, experimental results show that the models trained on synthetic data this way can produce high quality audio displaying accent transfer, while preserving speaker characteristics such as speaking style.

The performance of anytime algorithms can be improved by simultaneously solving several instances of algorithm-problem pairs. These pairs may include different instances of a problem (such as starting from a different initial state), different algorithms (if several alternatives exist), or several runs of the same algorithm (for non-deterministic algorithms). In this paper we present a methodology for designing an optimal scheduling policy based on the statistical characteristics of the algorithms involved. We formally analyze the case where the processes share resources (a single-processor model), and provide an algorithm for optimal scheduling. We analyze, theoretically and empirically, the behavior of our scheduling algorithm for various distribution types. Finally, we present empirical results of applying our scheduling algorithm to the Latin Square problem.

Keyword-based search engines are in widespread use today as a popular means for Web-based information retrieval. Although such systems seem deceptively simple, a considerable amount of skill is required in order to satisfy non-trivial information needs. This paper presents a new conceptual paradigm for performing search in context, that largely automates the search process, providing even non-professional users with highly relevant results. This paradigm is implemented in practice in the IntelliZap system, where search is initiated from a text query marked by the user in a document she views, and is guided by the text surrounding the marked query in that document ("the context"). The context-driven information retrieval process involves semantic keyword extraction and clustering to automatically generate new, augmented queries. The latter are submitted to a host of general and domain-specific search engines. Search results are then semantically reranked, using context. Experimental results testify that using context to guide search, effectively offers even inexperienced users an advanced search tool on the Web.

Article Share on Placing search in context: the concept revisited Authors: Lev Finkelstein Zapper Technologies Inc., 3 Azrieli Center, Tel Aviv 67023, Israel Zapper Technologies Inc., 3 Azrieli Center, Tel Aviv 67023, IsraelView Profile , Evgeniy Gabrilovich Zapper Technologies Inc., 3 Azrieli Center, Tel Aviv 67023, Israel Zapper Technologies Inc., 3 Azrieli Center, Tel Aviv 67023, IsraelView Profile , Yossi Matias Zapper Technologies Inc., 3 Azrieli Center, Tel Aviv 67023, Israel Zapper Technologies Inc., 3 Azrieli Center, Tel Aviv 67023, IsraelView Profile , Ehud Rivlin Zapper Technologies Inc., 3 Azrieli Center, Tel Aviv 67023, Israel Zapper Technologies Inc., 3 Azrieli Center, Tel Aviv 67023, IsraelView Profile , Zach Solan Zapper Technologies Inc., 3 Azrieli Center, Tel Aviv 67023, Israel Zapper Technologies Inc., 3 Azrieli Center, Tel Aviv 67023, IsraelView Profile , Gadi Wolfman Zapper Technologies Inc., 3 Azrieli Center, Tel Aviv 67023, Israel Zapper Technologies Inc., 3 Azrieli Center, Tel Aviv 67023, IsraelView Profile , Eytan Ruppin Zapper Technologies Inc., 3 Azrieli Center, Tel Aviv 67023, Israel Zapper Technologies Inc., 3 Azrieli Center, Tel Aviv 67023, IsraelView Profile Authors Info & Claims WWW '01: Proceedings of the 10th international conference on World Wide WebMay 2001 Pages 406–414https://doi.org/10.1145/371920.372094Online:01 April 2001Publication History 319citation2,268DownloadsMetricsTotal Citations319Total Downloads2,268Last 12 Months193Last 6 weeks26 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

One of the most common mechanisms used for speeding up problem solvers is macro-learning. Macros are sequences of basic operators acquired during problem solving. Macros are used by the problem solver as if they were basic operators. The major problem that macro-learning presents is the vast number of macros that are available for acquisition. Macros increase the branching factor of the search space and can severely degrade problem-solving efficiency. To make macro learning useful, a program must be selective in acquiring and utilizing macros. This paper describes a general method for selective acquisition of macros. Solvable training problems are generated in increasing order of difficulty. The only macros acquired are those that take the problem solver out of a local minimum to a better state. The utility of the method is demonstrated in several domains, including the domain of NxN sliding-tile puzzles. After learning on small puzzles, the system is able to efficiently solve puzzles of any size.