Cross-Linguistic Rhythmic and Spectral Feature-Based Analysis of Nyishi and Adi: Two Under-Resourced Languages of Arunachal Pradesh

TL;DR

This study analyzes acoustic features of Nyishi and Adi, two under-resourced Arunachal Pradesh languages, revealing hierarchical linguistic differentiation through rhythm and spectral features with high classification accuracy.

Contribution

It introduces a frequency domain framework combining rhythm formant analysis and spectral features to differentiate closely related languages.

Findings

Hierarchical pattern of linguistic differentiation identified

Rhythm features achieved 84-85% classification accuracy

Fusion of spectral features improved accuracy to over 93%

Abstract

Under-resourced languages remain underrepresented in quantitative rhythm research,particularly in systematic intra-branch analysis of acoustic differentiation within closely related linguistic groups.This study investigates acoustic differentiation within the Tani language subgroup by examining speech rhythm in Nyishi and Adi,two under-resourced Tani languages spoken in Arunachal Pradesh,North-East India,using a frequency domain framework based on amplitude modulation(AM) low-frequency(LF) spectrum analysis,commonly referred to as rhythm formant analysis(RFA).The analysis is designed to identify whether intra-branch differentiation follows a hierarchical pattern across rhythmic and spectral domains.From the LF modulation spectrum,three rhythm formant features were derived:Number of Dominant peaks(NDP),Mean Frequency of Dominant Peaks(MFDP),and Variance of Dominant Frequencies(VFDP).In addition,Discrete Cosine Transform (DCT)coefficients and Mel Frequency Cepstral Coefficient(MFCC) were extracted to characterise the spectral modulation structure and broad spectral organisation of the speech signal.Statistical modelling reveals a hierarchical pattern of differentiation,where rhythmic features show consistent but moderate separation,with Nyishi exhibiting higher dominant modulation frequencies as well as greater dispersion than Adi.Classification experiments further support this hierarchy,with rhythm-only features achieved approximately 84-85% classification accuracy.Fusion using MFCC representations improved performance to 90.9% classification accuracy using support vector machine (SVM) and 93.96% using multilayer perceptron (MLP).These findings demonstrate that rhythmic and spectral features encode complementary levels of linguistic variations,with low frequency modulation capturing constrained macro temporal structure and spectral features reflecting finer phonological differentiation.