Manipuri is a low-resource, tonal language spoken predominantly in Manipur, a northeastern state of India. It has two tones - level and falling tones. For an acceptable Automatic Speech Recognition (ASR) system, integration of tonal information from a robust Tone Recognition model is essential. Research work on ASR has been done on Asian, African and Indo-European tonal languages such as Mandarin, Thai, Vietnamese and Chinese but Manipuri is largely unexplored. This paper focuses on the fundamental analysis of the developed hand-crafted tonal contrast dataset, ManiTo. It is observed that the height and slope of the pitch contour can be used to distinguish the two tones of the Manipuri language.

Automatic phoneme segmentation is an important problem in speech processing. It helps in improving the recognition quality by providing a proper segmentation information for phonemes or phonetic units. Inappropriate segmentation may lead to recognition falloff. The problem is essential not only for recognition but also for annotation purpose also. In general, segmentation algorithms rely on training large data sets where data is observed to find the patterns among them. But this process is not straight forward for languages that are under resourced because of less availability of datasets. In this paper, we propose a method that uses geometrical properties of waveform trajectory where intra signal variations are studied and used for segmentation. The method does not rely on large datasets for training. The geometric properties are extracted as linear structural changes in a raw waveform. The methods and findings of the study are presented.