A Fuzzy Logic Approach to Improve Phone Segmentation A Case Study of Dutch Language

Phone segmentation is an essential task for Automatic Speech Recognition (ASR) systems, which still lack in performance when compared to the ability of humans’ speech recognition. In this paper, we propose novel Fuzzy Logic (FL) based approaches for the prediction of phone durations using linguistic features. To the best of our knowledge, this is the first development and deployment of FL based approaches in the area of phone segmentation. In this study, we perform a case study on the Dutch IFA corpus, which consists of 50000 words. Different experiments are conducted on tuned FL Systems (FLSs) and Neural Networks (NNs). The experimental results show that FLSs are more efficient in phone duration prediction in comparison to their Neural Network counterparts. Furthermore, we observe that differentiating between the vowels and the consonants improves the performance of predictions, which can facilitate enhanced ASR systems. The FLS with the differentiation between vowels and consonants had an average Mean Average Precision Error of 43.3396% on a k=3 fold. We believe that this first attempt of the employment of FL based approaches will be an important step for a wider deployment of FL in the area of ASR systems.