Analysis of M anipuri Tones in M ani T o: A Tonal Contrast Database (2021)

Abstract: 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.

## introduction automatic speech recognition (asr) generates the transcript of a spoken speech. it plays a significant role in stopping the extinction of endangered languages and is also required for fostering economic growth and benefits. for improving the performance of asr, tone recognition carries a crucial role in tonal languages where tone distinguishes the meaning of the words. intensive work has been done on tonal languages like mandarin, thai, vietnamese, chinese, etc., @xcite ), but no work has been done on the asr of manipuri. manipuri is one of the indian tibeto-burman languages spoken in manipur, a northeastern state of india. it is a tonal language in which the tone differentiates the word's meaning. so, robust tone recognition can enhance the speech-understanding tasks of manipuri. it has its script known as meitei @xcite . the script has twentyseven main alphabets (mapung mayek), eight unreleased characters (lonsum mayek), eight vowel signs (cheitap mayek), three punctuation marks, including diacritics or tone marker @xcite and cheising mayek for the numerals. manipuri has two tones @xcite @xcite @xcite , level and falling tones. each one of the syllables in manipuri bears one of the two tones. the level tone is unmarked, while the falling tone is marked with / / in english representation and with the falling tone marker or lum mayek, "•" just after the syllable in the manipuri script. some of the tonal contrast words pair with their meanings are shown in figure 1 . sl. no. falling tone meaning level tone meaning 1 un• /ùn/ skin un /un/ ice, snow 2 in• /ìn/ push in /in/ follow 3 toh• /t h ò / ŋ door toh /t h o / ŋ bridge 4 mi• /mì/ man mi /mi/ spider 5 pu •b /pùb / ə to borrow pu b /pubə/ to bring 6 ca • /cà/ wax ca /ca/ tea 7 i• /ì/ blood i /i/ thatch 8 koi• /k h òi/ navel koi /k h oi/ bee/ fishing hook 9 la • /là/ wide basket la /la/ banana leaf 10 sih• /sìŋ/ firewood sih /si / ŋ ginger 11 sm• /s m/ ə ̀hair sm /səm/ basket 12 mh• /m / ə ̀ŋ dream mh /mə / ŋ grave figure 1: list of tonal contrast word pairs with their meaning. 2 related works internationally, progressive work on tone recognition has been done for tonal languages like mandarin, thai, cantonese, and vietnamese for the last three decades. (linkai et al., 2021) recognized lexical tones in mandarin speech on the 863 corpora using a model that integrates multiple features at a different scale. experimental results showed that their technique achieved a tone error rate of 10.5%. (nguyen et al., 2016) proposed an acoustic model using the tone feature to investigate the effect of tone in the vietnamese large vocabulary continuous speech. the result improved the perception of phonemes by 19.25% against the non-tonal phonemes system. (krittakom and narissara, 2015) analyzed neuro-fuzzy based approach to recognize thai speech. their dataset consisted of eight thai words recorded in different environments. the result showed that the system was robust to noise and could yield higher recognition than other recognizers. in india, some work has been done on the tonal languages punjabi and mizo. (gogoi et al., 2020) proposed a technique for mizo tone recognition using support vector machine (svm) and deep neural network (dnn) based classifiers. the experiment is performed on a dataset obtained from nineteen speakers. it achieved an accuracy of 73.39% for the svm model and 74.11% for the dnn model. (jyoti and achyuta, 2020) proposed an asr system based on pitch-dependent features and the probability of voicing estimated features. the results significantly improved the word error rate of the system. however, there is no work done on the asr of the manipuri language, which motivates us to build a dataset that contains the tonal contrast word pairs of manipuri and examine the tone information to develop a robust asr system for manipuri. ## manipuri tonal contrast dataset a manipuri tonal contrast dataset, manito consisting of 3000 speech samples collected from 6 native speakers, 3 males and 3 females aged 20-35, is developed. the upper portion of the flowchart in figure 2 shows the steps for the development of manito. the corpus contains a total of 50 tonal contrast word pairs collected from different sources @xcite @xcite @xcite , five utterances of each pair recorded using cool edit 2000 in a laboratory as well as in a quiet office environment. the recorded speech is further examined and manually segmented, keeping 1000 samples of silence at the prefix and suffix of the speech sample and saved as .wav format. each file has the naming convention: word-name tonetype utteranceid speakerid. for example, un f 2 3.wav means the particular speech sample is the falling tone"un" sound spoken by speaker 3, second utterance. the corpus manito consists of hand-crafted labeled speech data of size 273mb. ## experimental analysis praat, version 6.1.51 @xcite tool is used to analyze speech data in man-ito. the lower portion of the flowchart in figure 2 shows the steps for analyzing the speech data.