Back to the Future: Logic and Machine Learning

In this paper we argue that since the beginning of the natural language processing or computational linguistics there has been a strong connection between logic and machine learning. First of all, there is something logical about language or linguistic about logic. Secondly, we argue that rather than distinguishing between logic and machine learning, a more useful distinction is between top-down approaches and data-driven approaches. Examining some recent approaches in deep learning we argue that they incorporate both properties and this is the reason for their very successful adoption to solve several problems within language technology

Automatic Quality Estimation for ASR System Combination

Recognizer Output Voting Error Reduction (ROVER) has been widely used for system combination in automatic speech recognition (ASR). In order to select the most appropriate words to insert at each position in the output transcriptions, some ROVER extensions rely on critical information such as confidence scores and other ASR decoder features. This information, which is not always available, highly depends on the decoding process and sometimes tends to over estimate the real quality of the recognized words. In this paper we propose a novel variant of ROVER that takes advantage of ASR quality estimation (QE) for ranking the transcriptions at "segment level" instead of: i) relying on confidence scores, or ii) feeding ROVER with randomly ordered hypotheses. We first introduce an effective set of features to compensate for the absence of ASR decoder information. Then, we apply QE techniques to perform accurate hypothesis ranking at segment-level before starting the fusion process. The evaluation is carried out on two different tasks, in which we respectively combine hypotheses coming from independent ASR systems and multi-microphone recordings. In both tasks, it is assumed that the ASR decoder information is not available. The proposed approach significantly outperforms standard ROVER and it is competitive with two strong oracles that e xploit prior knowledge about the real quality of the hypotheses to be combined. Compared to standard ROVER, the abs olute WER improvements in the two evaluation scenarios range from 0.5% to 7.3%

Integrating Automatic Transcription into the Language Documentation Workflow: Experiments with Na Data and the Persephone Toolkit

Automatic speech recognition tools have potential for facilitating language documentation, but in practice these tools remain little-used by linguists for a variety of reasons, such as that the technology is still new (and evolving rapidly), user-friendly interfaces are still under development, and case studies demonstrating the practical usefulness of automatic recognition in a low-resource setting remain few. This article reports on a success story in integrating automatic transcription into the language documentation workflow, specifically for Yongning Na, a language of Southwest China. Using Persephone, an open-source toolkit, a single-speaker speech transcription tool was trained over five hours of manually transcribed speech. The experiments found that this method can achieve a remarkably low error rate (on the order of 17%), and that automatic transcriptions were useful as a canvas for the linguist. The present report is intended for linguists with little or no knowledge of speech processing. It aims to provide insights into (i) the way the tool operates and (ii) the process of collaborating with natural language processing specialists. Practical recommendations are offered on how to anticipate the requirements of this type of technology from the early stages of data collection in the field.National Foreign Language Resource Cente

Supporting language diversity of European MOOCs with the EMMA platform

This paper introduces the cross-language support of the EMMA MOOC platform. Based on a discussion of language diversity in Europe we introduce the development and evaluation of automated translation of texts and subtitling of videos from Dutch into English. The development of an Automatic Speech Recognition (ASR) system and a Statistical Machine Translation (SMT) system is described. The resources employed and evaluation approach is introduced. Initial evaluation results are presented. Finally, we provide an outlook into future research and development.This work is partially funded by the EU under the Competitiveness and Innovation Framework Program 2007- 2017 (CIP) in the European Multiple MOOC Aggregator (EMMA) project. Grant Agreement no. 621030

CAPT를 위한 발음 변이 분석 및 CycleGAN 기반 피드백 생성

학위논문(박사)--서울대학교 대학원 :인문대학 협동과정 인지과학전공,2020. 2. 정민화.Despite the growing popularity in learning Korean as a foreign language and the rapid development in language learning applications, the existing computer-assisted pronunciation training (CAPT) systems in Korean do not utilize linguistic characteristics of non-native Korean speech. Pronunciation variations in non-native speech are far more diverse than those observed in native speech, which may pose a difficulty in combining such knowledge in an automatic system. Moreover, most of the existing methods rely on feature extraction results from signal processing, prosodic analysis, and natural language processing techniques. Such methods entail limitations since they necessarily depend on finding the right features for the task and the extraction accuracies. This thesis presents a new approach for corrective feedback generation in a CAPT system, in which pronunciation variation patterns and linguistic correlates with accentedness are analyzed and combined with a deep neural network approach, so that feature engineering efforts are minimized while maintaining the linguistically important factors for the corrective feedback generation task. Investigations on non-native Korean speech characteristics in contrast with those of native speakers, and their correlation with accentedness judgement show that both segmental and prosodic variations are important factors in a Korean CAPT system. The present thesis argues that the feedback generation task can be interpreted as a style transfer problem, and proposes to evaluate the idea using generative adversarial network. A corrective feedback generation model is trained on 65,100 read utterances by 217 non-native speakers of 27 mother tongue backgrounds. The features are automatically learnt in an unsupervised way in an auxiliary classifier CycleGAN setting, in which the generator learns to map a foreign accented speech to native speech distributions. In order to inject linguistic knowledge into the network, an auxiliary classifier is trained so that the feedback also identifies the linguistic error types that were defined in the first half of the thesis. The proposed approach generates a corrected version the speech using the learners own voice, outperforming the conventional Pitch-Synchronous Overlap-and-Add method.외국어로서의 한국어 교육에 대한 관심이 고조되어 한국어 학습자의 수가 크게 증가하고 있으며, 음성언어처리 기술을 적용한 컴퓨터 기반 발음 교육(Computer-Assisted Pronunciation Training; CAPT) 어플리케이션에 대한 연구 또한 적극적으로 이루어지고 있다. 그럼에도 불구하고 현존하는 한국어 말하기 교육 시스템은 외국인의 한국어에 대한 언어학적 특징을 충분히 활용하지 않고 있으며, 최신 언어처리 기술 또한 적용되지 않고 있는 실정이다. 가능한 원인으로써는 외국인 발화 한국어 현상에 대한 분석이 충분하게 이루어지지 않았다는 점, 그리고 관련 연구가 있어도 이를 자동화된 시스템에 반영하기에는 고도화된 연구가 필요하다는 점이 있다. 뿐만 아니라 CAPT 기술 전반적으로는 신호처리, 운율 분석, 자연어처리 기법과 같은 특징 추출에 의존하고 있어서 적합한 특징을 찾고 이를 정확하게 추출하는 데에 많은 시간과 노력이 필요한 실정이다. 이는 최신 딥러닝 기반 언어처리 기술을 활용함으로써 이 과정 또한 발전의 여지가 많다는 바를 시사한다. 따라서 본 연구는 먼저 CAPT 시스템 개발에 있어 발음 변이 양상과 언어학적 상관관계를 분석하였다. 외국인 화자들의 낭독체 변이 양상과 한국어 원어민 화자들의 낭독체 변이 양상을 대조하고 주요한 변이를 확인한 후, 상관관계 분석을 통하여 의사소통에 영향을 미치는 중요도를 파악하였다. 그 결과, 종성 삭제와 3중 대립의 혼동, 초분절 관련 오류가 발생할 경우 피드백 생성에 우선적으로 반영하는 것이 필요하다는 것이 확인되었다. 교정된 피드백을 자동으로 생성하는 것은 CAPT 시스템의 중요한 과제 중 하나이다. 본 연구는 이 과제가 발화의 스타일 변화의 문제로 해석이 가능하다고 보았으며, 생성적 적대 신경망 (Cycle-consistent Generative Adversarial Network; CycleGAN) 구조에서 모델링하는 것을 제안하였다. GAN 네트워크의 생성모델은 비원어민 발화의 분포와 원어민 발화 분포의 매핑을 학습하며, Cycle consistency 손실함수를 사용함으로써 발화간 전반적인 구조를 유지함과 동시에 과도한 교정을 방지하였다. 별도의 특징 추출 과정이 없이 필요한 특징들이 CycleGAN 프레임워크에서 무감독 방법으로 스스로 학습되는 방법으로, 언어 확장이 용이한 방법이다. 언어학적 분석에서 드러난 주요한 변이들 간의 우선순위는 Auxiliary Classifier CycleGAN 구조에서 모델링하는 것을 제안하였다. 이 방법은 기존의 CycleGAN에 지식을 접목시켜 피드백 음성을 생성함과 동시에 해당 피드백이 어떤 유형의 오류인지 분류하는 문제를 수행한다. 이는 도메인 지식이 교정 피드백 생성 단계까지 유지되고 통제가 가능하다는 장점이 있다는 데에 그 의의가 있다. 본 연구에서 제안한 방법을 평가하기 위해서 27개의 모국어를 갖는 217명의 유의미 어휘 발화 65,100개로 피드백 자동 생성 모델을 훈련하고, 개선 여부 및 정도에 대한 지각 평가를 수행하였다. 제안된 방법을 사용하였을 때 학습자 본인의 목소리를 유지한 채 교정된 발음으로 변환하는 것이 가능하며, 전통적인 방법인 음높이 동기식 중첩가산 (Pitch-Synchronous Overlap-and-Add) 알고리즘을 사용하는 방법에 비해 상대 개선률 16.67%이 확인되었다.Chapter 1. Introduction 1 1.1. Motivation 1 1.1.1. An Overview of CAPT Systems 3 1.1.2. Survey of existing Korean CAPT Systems 5 1.2. Problem Statement 7 1.3. Thesis Structure 7 Chapter 2. Pronunciation Analysis of Korean Produced by Chinese 9 2.1. Comparison between Korean and Chinese 11 2.1.1. Phonetic and Syllable Structure Comparisons 11 2.1.2. Phonological Comparisons 14 2.2. Related Works 16 2.3. Proposed Analysis Method 19 2.3.1. Corpus 19 2.3.2. Transcribers and Agreement Rates 22 2.4. Salient Pronunciation Variations 22 2.4.1. Segmental Variation Patterns 22 2.4.1.1. Discussions 25 2.4.2. Phonological Variation Patterns 26 2.4.1.2. Discussions 27 2.5. Summary 29 Chapter 3. Correlation Analysis of Pronunciation Variations and Human Evaluation 30 3.1. Related Works 31 3.1.1. Criteria used in L2 Speech 31 3.1.2. Criteria used in L2 Korean Speech 32 3.2. Proposed Human Evaluation Method 36 3.2.1. Reading Prompt Design 36 3.2.2. Evaluation Criteria Design 37 3.2.3. Raters and Agreement Rates 40 3.3. Linguistic Factors Affecting L2 Korean Accentedness 41 3.3.1. Pearsons Correlation Analysis 41 3.3.2. Discussions 42 3.3.3. Implications for Automatic Feedback Generation 44 3.4. Summary 45 Chapter 4. Corrective Feedback Generation for CAPT 46 4.1. Related Works 46 4.1.1. Prosody Transplantation 47 4.1.2. Recent Speech Conversion Methods 49 4.1.3. Evaluation of Corrective Feedback 50 4.2. Proposed Method: Corrective Feedback as a Style Transfer 51 4.2.1. Speech Analysis at Spectral Domain 53 4.2.2. Self-imitative Learning 55 4.2.3. An Analogy: CAPT System and GAN Architecture 57 4.3. Generative Adversarial Networks 59 4.3.1. Conditional GAN 61 4.3.2. CycleGAN 62 4.4. Experiment 63 4.4.1. Corpus 64 4.4.2. Baseline Implementation 65 4.4.3. Adversarial Training Implementation 65 4.4.4. Spectrogram-to-Spectrogram Training 66 4.5. Results and Evaluation 69 4.5.1. Spectrogram Generation Results 69 4.5.2. Perceptual Evaluation 70 4.5.3. Discussions 72 4.6. Summary 74 Chapter 5. Integration of Linguistic Knowledge in an Auxiliary Classifier CycleGAN for Feedback Generation 75 5.1. Linguistic Class Selection 75 5.2. Auxiliary Classifier CycleGAN Design 77 5.3. Experiment and Results 80 5.3.1. Corpus 80 5.3.2. Feature Annotations 81 5.3.3. Experiment Setup 81 5.3.4. Results 82 5.4. Summary 84 Chapter 6. Conclusion 86 6.1. Thesis Results 86 6.2. Thesis Contributions 88 6.3. Recommendations for Future Work 89 Bibliography 91 Appendix 107 Abstract in Korean 117 Acknowledgments 120Docto

Computer-Based Data Processing and Management for Blackfoot Phonetics and Phonology

More than half of the 6000 world languages have never been adequately described. We propose to create a database system to automatically capture and manage interested sound clips in Blackfoot (an endangered language spoken in Alberta, Canada, and Montana) for a phonetic and phonological analysis. Taking Blackfoot speeches as input, the system generates a list of audio clips containing a sequence of sounds or certain accent patterns based on research interests. Existing computational linguistic techniques such as information processing and artificial intelligence are extended to tackle issues specific to Blackfoot linguistics, and database techniques are adopted to support better data management and linguistic queries. This project is innovative because application of technology in Native American phonetics and phonology is underdeveloped. It enhances humanity with the digital framework to document and analyze endangered languages and can also benefit the research in other languages

Robust Neural Machine Translation

This thesis aims for general robust Neural Machine Translation (NMT) that is agnostic to the test domain. NMT has achieved high quality on benchmarks with closed datasets such as WMT and NIST but can fail when the translation input contains noise due to, for example, mismatched domains or spelling errors. The standard solution is to apply domain adaptation or data augmentation to build a domain-dependent system. However, in real life, the input noise varies in a wide range of domains and types, which is unknown in the training phase. This thesis introduces five general approaches to improve NMT accuracy and robustness, where three of them are invariant to models, test domains, and noise types. First, we describe a novel unsupervised text normalization framework Lex-Var, to reduce the lexical variations for NMT. Then, we apply the phonetic encoding as auxiliary linguistic information and obtained very significant (5 BLEU point) improvement in translation quality and robustness. Furthermore, we introduce the random clustering encoding method based on our hypothesis of Semantic Diversity by Phonetics and generalizes to all languages. We also discussed two domain adaptation models for the known test domain. Finally, we provide a measurement of translation robustness based on the consistency of translation accuracy among samples and use it to evaluate our other methods. All these approaches are verified with extensive experiments across different languages and achieved significant and consistent improvements in translation quality and robustness over the state-of-the-art NMT