Unsupervised phoneme segmentation of continuous Arabic speech

The development of a speech recognition system for the Arabic language presents a significant challenge, mainly due to the limited availability of digital resources specific to this language. To achieve vocabulary-independent speech recognition, it is essential to split a given speech into smaller units known as phonemes or syllables. This process is what we call speech segmentation; it plays a crucial role in accurately recognizing and understanding speech patterns. Many speech segmentation techniques have been developed, relying on linguistic information such as phonetic transcription. However, for real-time systems, phonetic transcription is not always available, especially for low-resource languages like Arabic. In this paper, we addressed the problem of unsupervised segmentation for continuous Arabic speech based on two distinct approaches: spectral contrast and the first derivative of Mel Frequency Cepstrum Coefficients ( \(\Delta\) $\mathrm{\Delta }$ -MFCCs), incorporating an adaptive threshold to determine the phoneme boundaries without needing any external knowledge. The intersection-over-union (IoU) was integrated into our study to match reference boundaries and the generated borders. As far as we know, the majority of works use this technique for image processing; we effectively adapted it to the realm of speech processing, accomplishing a noteworthy level of matching success. The Arabic speech corpus was used to evaluate the efficacy of our proposed methods. By comparing the performance of our methods to other approaches, we were able to assess their strengths. Notably, our method, which made use of \(\Delta\) $\mathrm{\Delta }$ -MFCCs, showcased a substantial performance improvement of 14%.