Felps Daniel, Bortfeld Heather, Gutierrez-Osuna Ricardo
Department of Computer Science, Texas A&M University, 3112 TAMU, College Station, TX 77843-3112, USA.
Speech Commun. 2009 Oct;51(10):920-932. doi: 10.1016/j.specom.2008.11.004.
Learners of a second language practice their pronunciation by listening to and imitating utterances from native speakers. Recent research has shown that choosing a well-matched native speaker to imitate can have a positive impact on pronunciation training. Here we propose a voice-transformation technique that can be used to generate the (arguably) ideal voice to imitate: the own voice of the learner with a native accent. Our work extends previous research, which suggests that providing learners with prosodically corrected versions of their utterances can be a suitable form of feedback in computer assisted pronunciation training. Our technique provides a conversion of both prosodic and segmental characteristics by means of a pitch-synchronous decomposition of speech into glottal excitation and spectral envelope. We apply the technique to a corpus containing parallel recordings of foreign-accented and native-accented utterances, and validate the resulting accent conversions through a series of perceptual experiments. Our results indicate that the technique can reduce foreign accentedness without significantly altering the voice quality properties of the foreign speaker. Finally, we propose a pedagogical strategy for integrating accent conversion as a form of behavioral shaping in computer assisted pronunciation training.
第二语言学习者通过听和模仿母语者的话语来练习发音。最近的研究表明，选择一个匹配度高的母语者进行模仿，对发音训练会产生积极影响。在此，我们提出一种语音转换技术，可用于生成（可以说是）理想的模仿语音：带有母语口音的学习者自己的声音。我们的工作扩展了先前的研究，该研究表明，在计算机辅助发音训练中，为学习者提供韵律校正后的话语版本可以作为一种合适的反馈形式。我们的技术通过将语音进行音高同步分解为声门激励和频谱包络，来实现韵律和音段特征的转换。我们将该技术应用于一个包含外国口音和母语口音话语平行录音的语料库，并通过一系列感知实验验证了由此产生的口音转换效果。我们的结果表明，该技术可以减少外国口音，而不会显著改变外国说话者的语音质量特性。最后，我们提出一种教学策略，将口音转换作为一种行为塑造形式融入计算机辅助发音训练中。
