Self-imitating Feedback Generation Using GAN for Computer-Assisted Pronunciation Training

Self-imitating feedback is an effective and learner-friendly method for non-native learners in Computer-Assisted Pronunciation Training. Acoustic characteristics in native utterances are extracted and transplanted onto learner's own speech input, and given back to the learner as a corrective feedback. Previous works focused on speech conversion using prosodic transplantation techniques based on PSOLA algorithm. Motivated by the visual differences found in spectrograms of native and non-native speeches, we investigated applying GAN to generate self-imitating feedback by utilizing generator's ability through adversarial training. Because this mapping is highly under-constrained, we also adopt cycle consistency loss to encourage the output to preserve the global structure, which is shared by native and non-native utterances. Trained on 97,200 spectrogram images of short utterances produced by native and non-native speakers of Korean, the generator is able to successfully transform the non-native spectrogram input to a spectrogram with properties of self-imitating feedback. Furthermore, the transformed spectrogram shows segmental corrections that cannot be obtained by prosodic transplantation. Perceptual test comparing the self-imitating and correcting abilities of our method with the baseline PSOLA method shows that the generative approach with cycle consistency loss is promising

Computational Approaches to Exploring Persian-Accented English

Methods involving phonetic speech recognition are discussed for detecting Persian-accented English. These methods offer promise for both the identification and mitigation of L2 pronunciation errors. Pronunciation errors, both segmental and suprasegmental, particular to Persian speakers of English are discussed

Disentangling accent from comprehensibility

The goal of this study was to determine which linguistic aspects of second language speech are related to accent and which to comprehensibility. To address this goal, 19 different speech measures in the oral productions of 40 native French speakers of English were examined in relation to accent and comprehensibility, as rated by 60 novice raters and three experienced teachers. Results showed that both constructs were associated with many speech measures, but that accent was uniquely related to aspects of phonology, including rhythm and segmental and syllable structure accuracy, while comprehensibility was chiefly linked to grammatical accuracy and lexical richness

Automatic generation of audio content for open learning resources

This paper describes how digital talking books (DTBs) with embedded functionality for learners can be generated from content structured according to the OU OpenLearn schema. It includes examples showing how a software transformation developed from open source components can be used to remix OpenLearn content, and discusses issues concerning the generation of synthesised speech for educational purposes. Factors which may affect the quality of a learner's experience with open educational audio resources are identified, and in conclusion plans for testing the effect of these factors are outlined

Automatic Pronunciation Assessment -- A Review

Pronunciation assessment and its application in computer-aided pronunciation training (CAPT) have seen impressive progress in recent years. With the rapid growth in language processing and deep learning over the past few years, there is a need for an updated review. In this paper, we review methods employed in pronunciation assessment for both phonemic and prosodic. We categorize the main challenges observed in prominent research trends, and highlight existing limitations, and available resources. This is followed by a discussion of the remaining challenges and possible directions for future work.Comment: 9 pages, accepted to EMNLP Finding

Machine learning approaches to improving mispronunciation detection on an imbalanced corpus

This thesis reports the investigations into the task of phone-level pronunciation error detection, the performance of which is heavily affected by the imbalanced distribution of the classes in a manually annotated data set of non-native English (Read Aloud responses from the TOEFL Junior Pilot assessment). In order to address problems caused by this extreme class imbalance, two machine learning approaches, cost-sensitive learning and over-sampling, are explored to improve the classification performance. Specifically, approaches which assigned weights inversely proportional to class frequencies and synthetic minority over-sampling technique (SMOTE) were applied to a range of classifiers using feature sets that included information about the acoustic signal, the linguistic properties of the utterance, and word identity. Empirical experiments demonstrate that both balancing approaches lead to a substantial performance improvement (in terms of f1 score) over the baseline on this extremely imbalanced data set. In addition, this thesis also discusses which features are the most important and which classifiers are most effective for the task of identifying phone-level pronunciation errors in non-native speech