Analyzing temporal properties of speech trajectory using
graph structures towards speech recognition

Analyzing temporal properties of speech trajectory using
graph structures towards speech recognition

Graph eigenvalues
Graph signal processing
Speech analysis
Structural processing
Speech trajectory

Speech signal analysis aims to identify patterns within data to develop effective
recognition algorithms. This process primarily utilizes feature extraction
techniques such as linear predictive coding (LPC), linear predictive cepstral coefficients
(LPCCs), and Mel-frequency cepstral coefficients (MFCCs). These
features are crucial for constructing recognition algorithms that leverage both
statistical and deep learning methods. While deep learning models require extensive
datasets, they often prove unsuitable for low-resource languages. The
Hidden Markov model (HMM) is the most widely adopted statistical framework
in speech processing. However, HMMs are characterized by state-dependent
models, where each state interacts only with its neighboring states. This limitation
restricts HMMs from capturing long-term signal properties, highlighting the
need for addressing these constraints at the feature extraction stage. Most feature
extraction methods rely on short-term signal processing, which further limits
the comprehension of speech utterances. To overcome these limitations, alternative
methods are necessary to capture more comprehensive patterns. This paper
presents a graph-based approach for analyzing speech trajectories and their
temporal properties, which are subsequently validated using HMMs in speech
recognition tasks. Graph-based representations on a low-resource Telugu dataset
improve recognition accuracy by 13% while reducing processing time compared
to traditional LPC.

Parabattina Bhagath1, Malempati Shanmukha2, Gnana Nagasri Puthi2

Journal homepage: http://journal.uad.ac.id/index.php/TELKOMNIKA

Sep 10, 2025

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