Acoustic modeling for child speech is challenging due to the high acoustic variability caused by physiological differences in the vocal tract. The dearth of publicly available datasets makes the task more challenging. In this work, we propose a feature adaptation approach by exploiting adversarial multi-task training to minimize acoustic mismatch at the senone (tied triphone states) level between adult and child speech and leverage large amounts of transcribed adult speech. We validate the proposed method on three tasks: child speech recognition, child pronunciation assessment and child fluency score prediction. Empirical results indicate that our proposed approach consistently outperforms competitive baselines, achieving 7.7% relative error reduction on speech recognition and up to 25.2% relative gains on the evaluation tasks.

In computer-assisted pronunciation training (CAPT), the scarcity of large-scale non-native corpora and human expert annotations are two fundamental challenges to non-native acoustic modeling. Most existing approaches of acoustic modeling in CAPT are based on non-native corpora while there are so many living languages in the world. It is impractical to collect and annotate every non-native speech corpus considering different language pairs. In this work, we address non-native acoustic modeling (both on phonetic and articulatory level) based on transfer learning. In order to effectively train acoustic models of non-native speech without using such data, we propose to exploit two large native speech corpora of learner's native language (L1) and target language (L2) to model cross-lingual phenomena. This kind of transfer learning can provide a better feature representation of non-native speech. Experimental evaluations are carried out for Japanese speakers learning English. We first demonstrate the proposed acoustic-phone model achieves a lower word error rate in non-native speech recognition. It also improves the pronunciation error detection based on goodness of pronunciation (GOP) score. For diagnosis of pronunciation errors, the proposed acoustic-articulatory modeling method is effective for providing detailed feedback at the articulation level.

Articulatory feedback is effective for computer-assisted pronunciation training (CAPT) systems. This paper investigates efficient model learning methods for providing articulatory information to language learners. We first propose an articulatory attribute modeling method based on a multi-label learning scheme. Then, the models are further enhanced with a simple and effective training label correction method. These proposed methods are evaluated in three tasks: native attribute recognition, pronunciation error detection of non-native speech, and non-native speech recognition. Experimental results show that proposed methods significantly improve the conventional deep neural network (DNN) based articulatory models.

For effective articulatory feedback in computer-assisted pronunciation training (CAPT) systems, we address effective articulatory models of second language (L2) learners' speech without using such data, which is difficult to collect and annotate in a large scale. Context-dependent articulatory attributes (placement and manner of articulation) are modeled based on deep neural network (DNN). In order to efficiently train the non-native articulatory models, we exploit large speech corpora of native and target language to model inter-language phenomena. This multi-lingual learning is then combined with multi-task learning, which uses phone-classification as a sub-task. These methods are applied to Mandarin Chinese pronunciation learning by Japanese native speakers. Effects are confirmed in the native attribute classification and pronunciation error detection of non-native speech.