A Comparative Study of Khasi Speech Recognition Systems with Recurrent Neural Network-Based Language Model

被引：0

作者：

Deepajothi, S. ^{[1
]}

Rao, Vuda Sreenivasa ^{[2
]}

Ambhika, C. ^{[3
]}

Mandala, Vishwanadham ^{[4
]}

Rao, R. V. V. N. Bheema ^{[5
]}

Kumar, Shailendra ^{[6
]}

Gera, Venkateswara Rao ^{[7
]}

Nagaraju, D. ^{[8
]}

机构：

[1] SRM Inst Sci & Technol, Dept Comp Technol, Kattankulathur 603203, Tamil Nadu, India

[2] Koneru Lakshmaiah Educ Fdn, Dept Comp Sci & Engn, Vaddeswaram 522302, Andhra Pradesh, India

[3] RMD Engn Coll, Dept AIML, Rsm Nagar, Kavarapetai, India

[4] Indiana Univ, Bloomington, IN USA

[5] Aditya Coll Engn & Technol, Dept Informat Technol, Surampalem, India

[6] Integral Univ Lucknow, Dept ECE, Lucknow 226026, Uttar Pradesh, India

[7] Kallam Haranadhareddy Inst Technol, Dept ECE, Guntur, India

[8] Sri Venkatesa Perumal Coll Engn & Technol, Dept CSE, Puttur, Andhra Pradesh, India

来源：

JOURNAL OF ELECTRICAL SYSTEMS | 2024年 / 20卷 / 06期

关键词：

Hidden Markov model; Language model; Perceptual linear prediction; Gaussian mixture model; Acoustic model;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper offers a comparative analysis of Khasi speech recognition systems utilizing a recurrent neural network-based language model (RNN-LM). Develop different acoustic models (AMs) to evaluate the optimal performance. This paper observed that using RNN-LM performed best than traditional other models. The wave surfer performs data processing followed by collecting the recorder based continuous speech database. Moreover, a minimization of word error rate (WER) in 2.83.8% range for major speech data and 2.4-3.5% for minor speech data. Additionally, two acoustic features are used, and from the experimental results, the Mel frequency cepstral coefficient (MFCC) yielded improved performance than the perceptual linear prediction (PLP).

引用

页码：1296 / 1305

页数：10

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