Min-Siong Liang

A Taiwanese text-to-speech system with applications to language learning

The paper describes a Taiwanese text-to-speech (TTS) system for Taiwanese language learning by using Taiwanese/Mandarin bilingual lexicon information. The TTS system is organized as three functional modules, which contain a text analysis module, a prosody module, and waveform synthesis modules. And then we set an experiment to evaluate the text analysis and tone-sandhi. An 89% labeling and 65% tone-sandhi accuracy rate can be achieved. With adopting proposed Taiwanese TTS component, talking electronic lexicon system, Taiwanese interactive spelling learning tool and Taiwanese TTS system can be built to help those who want to learn Taiwanese.

An efficient algorithm to select phonetically balanced scripts for constructing a speech corpus

Here, we describe an efficient algorithm to select phonetically balanced scripts for collecting a large-scale multilingual speech corpus. It is expected to collect a multilingual speech corpus covering three most frequently used languages in Taiwan, including Taiwanese (Min-nan), Hakka, and Mandarin Chinese. To achieve the objective, the first step is to construct a multilingual phonetic alphabet, namely Formosa phonetic alphabet (ForPA). In addition, the multilingual lexicons (Fomosa lexicons) are also important parts for building the corpus. Until now, this corpus containing 600 speakers speech of Taiwanese (Min-nan) and Mandarin Chinese has been finished and ready to release. There contains about 40 hours of speech in 247 thousand utterances in this release.

Data-Driven Approach to Pronunciation Error Detection for Computer Assisted Language Teaching

This paper describes an approach to pronunciation error detection for computer-assisted pronunciation teaching (CAPT). We focus on how to find the real pronunciation of the user. The data-driven based method was used to generate pronunciation errors hypotheses instead of knowledge-based method. In the experiment results, the error rate of pronunciation detection can achieve 10.56%. Finally, we applied this technique into our CAPT system.