A hierarchical automatic phoneme recognition model for <scp>Hindi‐Devanagari</scp> consonants using machine learning technique
نویسندگان
چکیده
Abstract A phoneme is perceptually the smallest distinct sound unit distinguished among words in a particular language. Every language has its own set of phonemes, and all are ordered sequences phonemes. Therefore, recognition essential to automatic speech (ASR) systems. Phonemes can be classified together using single machine learning (ML) model through direct classification (also known as baseline or flat classification) approach. However, it observed that performance such degrades with increase number classes. The challenge pronounced languages larger classes, like Hindi, which 48 In this paper, we propose speaker‐independent hierarchical approach for 33 Hindi‐Devanagari consonants/phonemes cepstral features ML techniques support vector (SVM), random forest (RF) fully connected deep neural network (DNN). approach, given into successive subgroups until class identified. To perform task, binary multi‐class classifier invoked each internal (non‐leaf) node hierarchy tree. Our identified pairs Optimal Feature Sets (based on mutual information ) best suitable decision 10‐fold cross‐validation help efficient classification. proposed leads better accuracy 57% improved compared non‐hierarchical, is,
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ژورنال
عنوان ژورنال: Expert Systems
سال: 2023
ISSN: ['0266-4720', '1468-0394']
DOI: https://doi.org/10.1111/exsy.13288