Personalized Filled-pause Generation with Group-wise Prediction Models

TL;DR

This paper introduces a group-wise prediction approach for generating personalized filled pauses in disfluent speech, improving the naturalness and accuracy of disfluency modeling in text generation.

Contribution

It proposes a novel group-dependent prediction model and a specialized loss function and embedding for better personalized filled pause prediction.

Findings

Group-dependent models outperform non-personalized models in FP prediction.

The new loss function enhances prediction accuracy.

Word embeddings tailored for FP prediction improve model performance.

Abstract

In this paper, we propose a method to generate personalized filled pauses (FPs) with group-wise prediction models. Compared with fluent text generation, disfluent text generation has not been widely explored. To generate more human-like texts, we addressed disfluent text generation. The usage of disfluency, such as FPs, rephrases, and word fragments, differs from speaker to speaker, and thus, the generation of personalized FPs is required. However, it is difficult to predict them because of the sparsity of position and the frequency difference between more and less frequently used FPs. Moreover, it is sometimes difficult to adapt FP prediction models to each speaker because of the large variation of the tendency within each speaker. To address these issues, we propose a method to build group-dependent prediction models by grouping speakers on the basis of their tendency to use FPs. This method does not require a large amount of data and time to train each speaker model. We further introduce a loss function and a word embedding model suitable for FP prediction. Our experimental results demonstrate that group-dependent models can predict FPs with higher scores than a non-personalized one and the introduced loss function and word embedding model improve the prediction performance.