Linne Ha


2018

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Community-Driven Crowdsourcing: Data Collection with Local Developers
Christina Funk | Michael Tseng | Ravindran Rajakumar | Linne Ha
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Building Open Javanese and Sundanese Corpora for Multilingual Text-to-Speech
Jaka Aris Eko Wibawa | Supheakmungkol Sarin | Chenfang Li | Knot Pipatsrisawat | Keshan Sodimana | Oddur Kjartansson | Alexander Gutkin | Martin Jansche | Linne Ha
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2016

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TTS for Low Resource Languages: A Bangla Synthesizer
Alexander Gutkin | Linne Ha | Martin Jansche | Knot Pipatsrisawat | Richard Sproat
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

We present a text-to-speech (TTS) system designed for the dialect of Bengali spoken in Bangladesh. This work is part of an ongoing effort to address the needs of under-resourced languages. We propose a process for streamlining the bootstrapping of TTS systems for under-resourced languages. First, we use crowdsourcing to collect the data from multiple ordinary speakers, each speaker recording small amount of sentences. Second, we leverage an existing text normalization system for a related language (Hindi) to bootstrap a linguistic front-end for Bangla. Third, we employ statistical techniques to construct multi-speaker acoustic models using Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) and Hidden Markov Model (HMM) approaches. We then describe our experiments that show that the resulting TTS voices score well in terms of their perceived quality as measured by Mean Opinion Score (MOS) evaluations.

2014

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A Database for Measuring Linguistic Information Content
Richard Sproat | Bruno Cartoni | HyunJeong Choe | David Huynh | Linne Ha | Ravindran Rajakumar | Evelyn Wenzel-Grondie
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Which languages convey the most information in a given amount of space? This is a question often asked of linguists, especially by engineers who often have some information theoretic measure of “information” in mind, but rarely define exactly how they would measure that information. The question is, in fact remarkably hard to answer, and many linguists consider it unanswerable. But it is a question that seems as if it ought to have an answer. If one had a database of close translations between a set of typologically diverse languages, with detailed marking of morphosyntactic and morphosemantic features, one could hope to quantify the differences between how these different languages convey information. Since no appropriate database exists we decided to construct one. The purpose of this paper is to present our work on the database, along with some preliminary results. We plan to release the dataset once complete.