In additive manufacturing of metals, numerous techniques have been employed to sense print defects. Among these, acoustic-based sensing has the advantage of low cost and shows the most potential to identify both external and internal defects as an in-situ monitoring system. Using acoustic signals, researchers have broadly investigated non-machine learning and machine learning-based approaches to identify defects like balling, micro defects, lack of fusion pores, keyhole pores, cracks, and porosity. While most of these works have shown promising results for laser-based AM systems, few have explored how acoustic signals can be used effectively for Wire Arc Additive Manufacturing (WAAM) defect detection. This paper proposes a methodology to construct machine learning (ML)-based models on identifying geometrically defective bead segments using acoustic signals during the WAAM process. Geometrically defective bead segment or geometric defect is a defect that causes voids in the final printed part due to incomplete fusion between two non-uniform overlapping bead segments. Such a defect is currently not explored in the literature. The proposed methodology uses a novel dataset labeling approach to identify good and bad bead segments based on an optimal threshold of the range of mean curvature. Furthermore, the methodology targets defective bead segments based on acoustic feature inputs like Principal Components (PC) or Mel Frequency Cepstral Coefficients (MFCC). To understand the resulting performance of the defect identification models constructed based on the proposed methodology, experiments are performed and tested on a variety of ML models (KNN, SVM, RF, NN, and CNN) based on the Inconel 718 material. The results show that the combinatorics of two acoustic input features and five ML models can be able to identify geometrically defective segments accurately with F1 score that ranges from 80% to 85%.