Advances in machine learning have made it possible to perform various text and speech processing tasks, such as automatic speech recognition (ASR), in an end-to-end (E2E) manner. E2E approaches utilizing pre-trained models are gaining attention for conserving training data and resources. However, most of their applications in ASR involve only one of either a pre-trained speech or a language model. This paper proposes integrating a pre-trained speech representation model and a large language model (LLM) for E2E ASR. The proposed model enables the optimization of the entire ASR process, including acoustic feature extraction and acoustic and language modeling, by combining pre-trained models with a bridge network and also enables the application of remarkable developments in LLM utilization, such as parameter-efficient domain adaptation and inference optimization. Experimental results demonstrate that the proposed model achieves a performance comparable to that of modern E2E ASR models by utilizing powerful pre-training models with the proposed integrated approach.

Multimodal language models that process both text and speech have a potential for applications in spoken dialogue systems. However, current models face two major challenges in response generation latency: (1) generating a spoken response requires the prior generation of a written response, and (2) speech sequences are significantly longer than text sequences. This study addresses these issues by extending the input and output sequences of the language model to support the parallel generation of text and speech. Our experiments on spoken question answering tasks demonstrate that our approach improves latency while maintaining the quality of response content. Additionally, we show that latency can be further reduced by generating speech in multiple sequences. Demo samples are available at https://rinnakk.github.io/research/publications/PSLM.

AI democratization aims to create a world in which the average person can utilize AI techniques. To achieve this goal, numerous research institutes have attempted to make their results accessible to the public. In particular, large pre-trained models trained on large-scale data have shown unprecedented potential, and their release has had a significant impact. However, most of the released models specialize in the English language, and thus, AI democratization in non-English-speaking communities is lagging significantly. To reduce this gap in AI access, we released Generative Pre-trained Transformer (GPT), Contrastive Language and Image Pre-training (CLIP), Stable Diffusion, and Hidden-unit Bidirectional Encoder Representations from Transformers (HuBERT) pre-trained in Japanese. By providing these models, users can freely interface with AI that aligns with Japanese cultural values and ensures the identity of Japanese culture, thus enhancing the democratization of AI. Additionally, experiments showed that pre-trained models specialized for Japanese can efficiently achieve high performance in Japanese tasks.

Argumentative dialogue is an important process where speakers discuss a specific theme for consensus building or decision making. In previous studies for generating consistent argumentative dialogue, retrieval-based methods with hand-crafted argumentation structures have been used. In this study, we propose a method to generate natural argumentative dialogues by combining an argumentation structure and language model. We trained the language model to rewrite a proposition of an argumentation structure on the basis of its information, such as keywords and stance, into the next utterance while considering its context, and we used the model to rewrite propositions in the argumentation structure. We manually evaluated the generated dialogues and found that the proposed method significantly improved the naturalness of dialogues without losing consistency of argumentation.

Creating chatbots to behave like real people is important in terms of believability. Errors in general chatbots and chatbots that follow a rough persona have been studied, but those in chatbots that behave like real people have not been thoroughly investigated. We collected a large amount of user interactions of a generation-based chatbot trained from large-scale dialogue data of a specific character, i.e., target person, and analyzed errors related to that person. We found that person-specific errors can be divided into two types: errors in attributes and those in relations, each of which can be divided into two levels: self and other. The correspondence with an existing taxonomy of errors was also investigated, and person-specific errors that should be addressed in the future were clarified.

This study investigates how the grounding process is composed and explores new interaction approaches that adapt to human cognitive processes that have not yet been significantly studied. The results of an experiment indicate that grounding through dialogue is mutually accepted among participants through holistic expressions and suggest that common ground among participants may not necessarily be formed in a bottom-up way through analytic expressions. These findings raise the possibility of a promising new approach to creating a human-like dialogue system that may be more suitable for natural human communication.

Building common ground with users is essential for dialogue agent systems and robots to interact naturally with people. While a few previous studies have investigated the process of building common ground in human-human dialogue, most of them have been conducted on the basis of text chat. In this study, we constructed a dialogue corpus to investigate the process of building common ground with a particular focus on the modality of dialogue and the social relationship between the participants in the process of building common ground, which are important but have not been investigated in the previous work. The results of our analysis suggest that adding the modality or developing the relationship between workers speeds up the building of common ground. Specifically, regarding the modality, the presence of video rather than only audio may unconsciously facilitate work, and as for the relationship, it is easier to convey information about emotions and turn-taking among friends than in first meetings. These findings and the corpus should prove useful for developing a system to support remote communication.

To develop a dialogue system that can build common ground with users, the process of building common ground through dialogue needs to be clarified. However, the studies on the process of building common ground have not been well conducted; much work has focused on finding the relationship between a dialogue in which users perform a collaborative task and its task performance represented by the final result of the task. In this study, to clarify the process of building common ground, we propose a data collection method for automatically recording the process of building common ground through a dialogue by using the intermediate result of a task. We collected 984 dialogues, and as a result of investigating the process of building common ground, we found that the process can be classified into several typical patterns and that conveying each worker’s understanding through affirmation of a counterpart’s utterances especially contributes to building common ground. In addition, toward dialogue systems that can build common ground, we conducted an automatic estimation of the degree of built common ground and found that its degree can be estimated quite accurately.

This paper concerns the problem of realizing consistent personalities in neural conversational modeling by using user generated question-answer pairs as training data. Using the framework of role play-based question answering, we collected single-turn question-answer pairs for particular characters from online users. Meta information was also collected such as emotion and intimacy related to question-answer pairs. We verified the quality of the collected data and, by subjective evaluation, we also verified their usefulness in training neural conversational models for generating utterances reflecting the meta information, especially emotion.

In dialogue systems, conveying understanding results of user utterances is important because it enables users to feel understood by the system. However, it is not clear what types of understanding results should be conveyed to users; some utterances may be offensive and some may be too commonsensical. In this paper, we explored the effect of conveying understanding results of user utterances in a chat-oriented dialogue system by an experiment using human subjects. As a result, we found that only certain types of understanding results, such as those related to a user’s permanent state, are effective to improve user satisfaction. This paper clarifies the types of understanding results that can be safely uttered by a system.