Nonparametric Bayesian Double Articulation Analyzerfor Direct Language Acquisition from Continuous Speech Signals
TABLE III: ARI for estimated latent letters and words
['Method', 'Letter ARI', 'Word ARI', 'AM', 'LM']
Check marks in the AM and LM columns indicate that the method used a pretrained acoustic model (AM) and the given true language model (LM), respectively. Letter ARI shows the ARI of phoneme clustering. A high Letter ARI means more accurate phoneme acquisition and recognition. Word ARI shows the ARI of word clustering. A higher Word ARI means more accurate word discovery and recognition. Each row corresponds to each method explained in the conditions. The results of “NPB-DAA” and “Conventional DAA” show the ARI averaged over 20 trials. In contrast, “NPB-DAA (MAP)” obtained the maximum a posteriori probability (MAP) of the 20 trials. An advantage of the NPB-DAA is that the method can calculate the posterior probability of a given dataset after the learning phase because the NPB-DAA is derived from a generative model, i.e., HDP-HLM, which integrates the language and acoustic models. In contrast with the conventional DAA and similar methods that do not have appropriate generative models, the NPB-DAA can obtain an appropriate learning result by referring to the probability. In this paper, we focused on a language acquisition method based on distributional cues and proposed a mathematical model for language acquisition. Obviously, distributional cues are not enough for more accurate language acquisition. The proposed HDP-HLM is a fully probabilistic generative model. Therefore, introducing other factors into consideration is relatively easier than for other heuristic models. This is also advantage of our approach. Combining prosodic and co-occurrence cues into the NPB-DAA, and obtaining a more accurate and more plausible constructive developmental language acquisition model is also a direction for future research.