Contextual Phonetic Pretraining for End-to-end Utterance-level Language and Speaker Recognition

Pretrained contextual word representations in NLP have greatly improved performance on various downstream tasks. For speech, we propose contextual frame representations that capture phonetic information at the acoustic frame level and can be used for utterance-level language, speaker, and speech recognition. These representations come from the frame-wise intermediate representations of an end-to-end, self-attentive ASR model (SAN-CTC) on spoken utterances. We first train the model on the Fisher English corpus with context-independent phoneme labels, then use its representations at inference time as features for task-specific models on the NIST LRE07 closed-set language recognition task and a Fisher speaker recognition task, giving significant improvements over the state-of-the-art on both (e.g., language EER of 4.68% on 3sec utterances, 23% relative reduction in speaker EER). Results remain competitive when using a novel dilated convolutional model for language recognition, or when ASR pretraining is done with character labels only.Comment: submitted to INTERSPEECH 201

Neural approaches to spoken content embedding

Comparing spoken segments is a central operation to speech processing. Traditional approaches in this area have favored frame-level dynamic programming algorithms, such as dynamic time warping, because they require no supervision, but they are limited in performance and efficiency. As an alternative, acoustic word embeddings -- fixed-dimensional vector representations of variable-length spoken word segments -- have begun to be considered for such tasks as well. However, the current space of such discriminative embedding models, training approaches, and their application to real-world downstream tasks is limited. We start by considering ``single-view" training losses where the goal is to learn an acoustic word embedding model that separates same-word and different-word spoken segment pairs. Then, we consider ``multi-view" contrastive losses. In this setting, acoustic word embeddings are learned jointly with embeddings of character sequences to generate acoustically grounded embeddings of written words, or acoustically grounded word embeddings. In this thesis, we contribute new discriminative acoustic word embedding (AWE) and acoustically grounded word embedding (AGWE) approaches based on recurrent neural networks (RNNs). We improve model training in terms of both efficiency and performance. We take these developments beyond English to several low-resource languages and show that multilingual training improves performance when labeled data is limited. We apply our embedding models, both monolingual and multilingual, to the downstream tasks of query-by-example speech search and automatic speech recognition. Finally, we show how our embedding approaches compare with and complement more recent self-supervised speech models.Comment: PhD thesi

음성언어 이해에서의 중의성 해소

학위논문(박사) -- 서울대학교대학원 : 공과대학 전기·정보공학부, 2022. 8. 김남수.언어의 중의성은 필연적이다. 그것은 언어가 의사 소통의 수단이지만, 모든 사람이 생각하는 어떤 개념이 완벽히 동일하게 전달될 수 없는 것에 기인한다. 이는 필연적인 요소이기도 하지만, 언어 이해에서 중의성은 종종 의사 소통의 단절이나 실패를 가져오기도 한다. 언어의 중의성에는 다양한 층위가 존재한다. 하지만, 모든 상황에서 중의성이 해소될 필요는 없다. 태스크마다, 도메인마다 다른 양상의 중의성이 존재하며, 이를 잘 정의하고 해소될 수 있는 중의성임을 파악한 후 중의적인 부분 간의 경계를 잘 정하는 것이 중요하다. 본고에서는 음성 언어 처리, 특히 의도 이해에 있어 어떤 양상의 중의성이 발생할 수 있는지 알아보고, 이를 해소하기 위한 연구를 진행한다. 이러한 현상은 다양한 언어에서 발생하지만, 그 정도 및 양상은 언어에 따라서 다르게 나타나는 경우가 많다. 우리의 연구에서 주목하는 부분은, 음성 언어에 담긴 정보량과 문자 언어의 정보량 차이로 인해 중의성이 발생하는 경우들이다. 본 연구는 운율(prosody)에 따라 문장 형식 및 의도가 다르게 표현되는 경우가 많은 한국어를 대상으로 진행된다. 한국어에서는 다양한 기능이 있는(multi-functional한) 종결어미(sentence ender), 빈번한 탈락 현상(pro-drop), 의문사 간섭(wh-intervention) 등으로 인해, 같은 텍스트가 여러 의도로 읽히는 현상이 발생하곤 한다. 이것이 의도 이해에 혼선을 가져올 수 있다는 데에 착안하여, 본 연구에서는 이러한 중의성을 먼저 정의하고, 중의적인 문장들을 감지할 수 있도록 말뭉치를 구축한다. 의도 이해를 위한 말뭉치를 구축하는 과정에서 문장의 지향성(directivity)과 수사성(rhetoricalness)이 고려된다. 이것은 음성 언어의 의도를 서술, 질문, 명령, 수사의문문, 그리고 수사명령문으로 구분하게 하는 기준이 된다. 본 연구에서는 기록된 음성 언어(spoken language)를 충분히 높은 일치도(kappa = 0.85)로 주석한 말뭉치를 이용해, 음성이 주어지지 않은 상황에서 중의적인 텍스트를 감지하는 데에 어떤 전략 혹은 언어 모델이 효과적인가를 보이고, 해당 태스크의 특징을 정성적으로 분석한다. 또한, 우리는 텍스트 층위에서만 중의성에 접근하지 않고, 실제로 음성이 주어진 상황에서 중의성 해소(disambiguation)가 가능한지를 알아보기 위해, 텍스트가 중의적인 발화들만으로 구성된 인공적인 음성 말뭉치를 설계하고 다양한 집중(attention) 기반 신경망(neural network) 모델들을 이용해 중의성을 해소한다. 이 과정에서 모델 기반 통사적/의미적 중의성 해소가 어떠한 경우에 가장 효과적인지 관찰하고, 인간의 언어 처리와 어떤 연관이 있는지에 대한 관점을 제시한다. 본 연구에서는 마지막으로, 위와 같은 절차로 의도 이해 과정에서의 중의성이 해소되었을 경우, 이를 어떻게 산업계 혹은 연구 단에서 활용할 수 있는가에 대한 간략한 로드맵을 제시한다. 텍스트에 기반한 중의성 파악과 음성 기반의 의도 이해 모듈을 통합한다면, 오류의 전파를 줄이면서도 효율적으로 중의성을 다룰 수 있는 시스템을 만들 수 있을 것이다. 이러한 시스템은 대화 매니저(dialogue manager)와 통합되어 간단한 대화(chit-chat)가 가능한 목적 지향 대화 시스템(task-oriented dialogue system)을 구축할 수도 있고, 단일 언어 조건(monolingual condition)을 넘어 음성 번역에서의 에러를 줄이는 데에 활용될 수도 있다. 우리는 본고를 통해, 운율에 민감한(prosody-sensitive) 언어에서 의도 이해를 위한 중의성 해소가 가능하며, 이를 산업 및 연구 단에서 활용할 수 있음을 보이고자 한다. 본 연구가 다른 언어 및 도메인에서도 고질적인 중의성 문제를 해소하는 데에 도움이 되길 바라며, 이를 위해 연구를 진행하는 데에 활용된 리소스, 결과물 및 코드들을 공유함으로써 학계의 발전에 이바지하고자 한다.Ambiguity in the language is inevitable. It is because, albeit language is a means of communication, a particular concept that everyone thinks of cannot be conveyed in a perfectly identical manner. As this is an inevitable factor, ambiguity in language understanding often leads to breakdown or failure of communication. There are various hierarchies of language ambiguity. However, not all ambiguity needs to be resolved. Different aspects of ambiguity exist for each domain and task, and it is crucial to define the boundary after recognizing the ambiguity that can be well-defined and resolved. In this dissertation, we investigate the types of ambiguity that appear in spoken language processing, especially in intention understanding, and conduct research to define and resolve it. Although this phenomenon occurs in various languages, its degree and aspect depend on the language investigated. The factor we focus on is cases where the ambiguity comes from the gap between the amount of information in the spoken language and the text. Here, we study the Korean language, which often shows different sentence structures and intentions depending on the prosody. In the Korean language, a text is often read with multiple intentions due to multi-functional sentence enders, frequent pro-drop, wh-intervention, etc. We first define this type of ambiguity and construct a corpus that helps detect ambiguous sentences, given that such utterances can be problematic for intention understanding. In constructing a corpus for intention understanding, we consider the directivity and rhetoricalness of a sentence. They make up a criterion for classifying the intention of spoken language into a statement, question, command, rhetorical question, and rhetorical command. Using the corpus annotated with sufficiently high agreement on a spoken language corpus, we show that colloquial corpus-based language models are effective in classifying ambiguous text given only textual data, and qualitatively analyze the characteristics of the task. We do not handle ambiguity only at the text level. To find out whether actual disambiguation is possible given a speech input, we design an artificial spoken language corpus composed only of ambiguous sentences, and resolve ambiguity with various attention-based neural network architectures. In this process, we observe that the ambiguity resolution is most effective when both textual and acoustic input co-attends each feature, especially when the audio processing module conveys attention information to the text module in a multi-hop manner. Finally, assuming the case that the ambiguity of intention understanding is resolved by proposed strategies, we present a brief roadmap of how the results can be utilized at the industry or research level. By integrating text-based ambiguity detection and speech-based intention understanding module, we can build a system that handles ambiguity efficiently while reducing error propagation. Such a system can be integrated with dialogue managers to make up a task-oriented dialogue system capable of chit-chat, or it can be used for error reduction in multilingual circumstances such as speech translation, beyond merely monolingual conditions. Throughout the dissertation, we want to show that ambiguity resolution for intention understanding in prosody-sensitive language can be achieved and can be utilized at the industry or research level. We hope that this study helps tackle chronic ambiguity issues in other languages ​​or other domains, linking linguistic science and engineering approaches.1 Introduction 1 1.1 Motivation 2 1.2 Research Goal 4 1.3 Outline of the Dissertation 5 2 Related Work 6 2.1 Spoken Language Understanding 6 2.2 Speech Act and Intention 8 2.2.1 Performatives and statements 8 2.2.2 Illocutionary act and speech act 9 2.2.3 Formal semantic approaches 11 2.3 Ambiguity of Intention Understanding in Korean 14 2.3.1 Ambiguities in language 14 2.3.2 Speech act and intention understanding in Korean 16 3 Ambiguity in Intention Understanding of Spoken Language 20 3.1 Intention Understanding and Ambiguity 20 3.2 Annotation Protocol 23 3.2.1 Fragments 24 3.2.2 Clear-cut cases 26 3.2.3 Intonation-dependent utterances 28 3.3 Data Construction . 32 3.3.1 Source scripts 32 3.3.2 Agreement 32 3.3.3 Augmentation 33 3.3.4 Train split 33 3.4 Experiments and Results 34 3.4.1 Models 34 3.4.2 Implementation 36 3.4.3 Results 37 3.5 Findings and Summary 44 3.5.1 Findings 44 3.5.2 Summary 45 4 Disambiguation of Speech Intention 47 4.1 Ambiguity Resolution 47 4.1.1 Prosody and syntax 48 4.1.2 Disambiguation with prosody 50 4.1.3 Approaches in SLU 50 4.2 Dataset Construction 51 4.2.1 Script generation 52 4.2.2 Label tagging 54 4.2.3 Recording 56 4.3 Experiments and Results 57 4.3.1 Models 57 4.3.2 Results 60 4.4 Summary 63 5 System Integration and Application 65 5.1 System Integration for Intention Identification 65 5.1.1 Proof of concept 65 5.1.2 Preliminary study 69 5.2 Application to Spoken Dialogue System 75 5.2.1 What is 'Free-running' 76 5.2.2 Omakase chatbot 76 5.3 Beyond Monolingual Approaches 84 5.3.1 Spoken language translation 85 5.3.2 Dataset 87 5.3.3 Analysis 94 5.3.4 Discussion 95 5.4 Summary 100 6 Conclusion and Future Work 103 Bibliography 105 Abstract (In Korean) 124 Acknowledgment 126박

Transferring speech-generic and depression-specific knowledge for Alzheimer's disease detection

The detection of Alzheimer's disease (AD) from spontaneous speech has attracted increasing attention while the sparsity of training data remains an important issue. This paper handles the issue by knowledge transfer, specifically from both speech-generic and depression-specific knowledge. The paper first studies sequential knowledge transfer from generic foundation models pretrained on large amounts of speech and text data. A block-wise analysis is performed for AD diagnosis based on the representations extracted from different intermediate blocks of different foundation models. Apart from the knowledge from speech-generic representations, this paper also proposes to simultaneously transfer the knowledge from a speech depression detection task based on the high comorbidity rates of depression and AD. A parallel knowledge transfer framework is studied that jointly learns the information shared between these two tasks. Experimental results show that the proposed method improves AD and depression detection, and produces a state-of-the-art F1 score of 0.928 for AD diagnosis on the commonly used ADReSSo dataset.Comment: 8 pages, 4 figures. Accepted by ASRU 202