Investigation of the Effect of MFCC Variation on the Convolutional Neural Network-based Speech Classification
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Md. Rakibul Hasan investigated the effect of Mel-frequency cepstral coefficient (MFCC) variation on convolutional neural network-based speech classification. He collected isolated vowel and word samples from Bengali speech and extracted MFCC features. A CNN model was trained on the MFCC data and achieved higher accuracy for vowel recognition compared to word recognition. Analysis showed vowels had less MFCC variation than words, contributing to their better classification performance by the CNN model.
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Main fundamental challenge for recent research work on speech based on science and technology is to understand and model the user variants in Spoken Languages. Users have their style of speaking, reliant on various factors, adding the dialect and accent of the speaker as well as the social and economic background of the speaker and contextual attributes like degree of knowledge between the listener, speaker and the position or rank of the speaking condition, from very normal to formal. In the past few decades, an extensive progress has been seen in automatically verifying the language of a speaker offered a sample speech. The main purpose of dialect verification is the recognition of a speaker’s region dialect, within a pre-determined language, offered the acoustic signal alone. DR (Dialect Recognition) is a main issue in particular, since even within the similar dialect and accent or register user change may occur. For illustration, In Spontaneous speech, few speakers tend to exhibit more optimizing and alteration of function words than others. The main issue of dialect recognition system has been viewed as challenging than that of language classification or recognition due to the maximum similarity among dialects of the similar language. While, dialects may differ in any dimensions of the linguistic spectrum such as syntactic, lexical, morphological, phonological differences, these changes are likely to be more indirect across dialects than those across languages such as Hindi, Punjabi and English etc.
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In the present-day communications speech signals get contaminated due to
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hard threshold which is not capable to derive satisfactory performance and a
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E0502 01 2327
This document reviews techniques used in spoken-word recognition systems. It discusses popular feature extraction techniques like MFCC, LPC, DWT, WPD that are used to represent speech signals in a compact form before classification. Classification techniques discussed are ANN, HMM, DTW, and VQ. The document provides a brief overview of each technique and their advantages. It also presents the generalized workflow of a spoken-word recognition system including stages of speech acquisition, pre-emphasis, feature extraction, modeling, classification, and output of recognized text.
Ijetcas14 426
This document presents a text-dependent speaker recognition system using neural networks that aims to improve recognition accuracy. It proposes changing the number of Mel Frequency Cepstral Coefficients (MFCCs) used in training. Voice Activity Detection is also used as a preprocessing step. Experimental results show recognition accuracy increases from 70.41% to a maximum of 92.91% as the number of MFCCs increases from 14 to 20, but then decreases with more MFCCs. The system is implemented on a Raspberry Pi for hardware acceleration.
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Investigation of the Effect of MFCC Variation on the Convolutional Neural Network-based Speech Classification
- 1. 5-7 June, Dhaka, Bangladesh Investigation of the Effect of MFCC Variation on the Convolutional Neural Network-based Speech Classification Md. Rakibul Hasan, Md. Mahbub Hasan Department of Electrical and Electronic Engineering Khulna University of Engineering & Technology Khulna-9203, Bangladesh
- 2. Outlines of the Talk MD. RAKIBUL HASAN, INVESTIGATION OF THE EFFECT OF MFCC VARIATION ON THE CNN-BASED SPEECH CLASSIFICATION 2 Relevant Theories Materials and Methods Results & Discussion Contributions and Conclusion Question & Answer Session MULTIMEDIA AND SIGNAL PROCESSING (II)
- 3. Relevant Theories 3 Sounds that are filtered by the shape of the vocal tract including tongue, teeth, etc. This shape determines what sound comes out. What is Speech? Vocal tract dynamics respond differently when speaking Vowels or Words MD. RAKIBUL HASAN, INVESTIGATION OF THE EFFECT OF MFCC VARIATION ON THE CNN-BASED SPEECH CLASSIFICATION MULTIMEDIA AND SIGNAL PROCESSING (II)
- 4. Materials and Methods 4 Chosen Isolated Vowels: /অ/ [/ɔ/], /আ/ [/a/], / ই/[/i/] , /উ/ [/u/], /ঋ/[/ri/], /এ/ [/e/], /ঐ/ [/oi/] Chosen Isolated Words: ব োতল, ন, কপি, ব োকোন, বেষ, সঠিক, উিরে Creation of Dataset Recorded by smart- phone Converted Stereo track to Mono Clipped into distinct speech tokens 40 data in each of the speech class MD. RAKIBUL HASAN, INVESTIGATION OF THE EFFECT OF MFCC VARIATION ON THE CNN-BASED SPEECH CLASSIFICATION MULTIMEDIA AND SIGNAL PROCESSING (II)
- 5. Materials and Methods (Contd.) 5 Feature Extraction Mel-Frequency Cepstral Coefficient (MFCC) librosa Python package Convolutional Neural Network Model Coding Environment: ‘Python 3.5’ on ‘Jupyter’ Notebook Library: Keras, an open source Neural Network Library Recognition Performance Loss Accuracy Confusion Matrix Cross-validation Score MD. RAKIBUL HASAN, INVESTIGATION OF THE EFFECT OF MFCC VARIATION ON THE CNN-BASED SPEECH CLASSIFICATION MULTIMEDIA AND SIGNAL PROCESSING (II)
- 6. Materials and Methods (Contd.) 6 Recognition Model Architecture Fig. 1: Architecture of the model. MD. RAKIBUL HASAN, INVESTIGATION OF THE EFFECT OF MFCC VARIATION ON THE CNN-BASED SPEECH CLASSIFICATION MULTIMEDIA AND SIGNAL PROCESSING (II)
- 7. Results & Discussion 7 Fig. 2: Loss and Accuracy Comparison. Performance Comparison between Vowel and Word Recognition Loss Accuracy MD. RAKIBUL HASAN, INVESTIGATION OF THE EFFECT OF MFCC VARIATION ON THE CNN-BASED SPEECH CLASSIFICATION MULTIMEDIA AND SIGNAL PROCESSING (II)
- 8. Results & Discussion (Contd.) 8 Fig. 3: Confusion Matrix Comparison. Vowel Word MD. RAKIBUL HASAN, INVESTIGATION OF THE EFFECT OF MFCC VARIATION ON THE CNN-BASED SPEECH CLASSIFICATION MULTIMEDIA AND SIGNAL PROCESSING (II)
- 9. Results & Discussion (Contd.) 9 Recognition Fold -1 Fold-2 Fold-3 Fold-4 Overall Accuracy Vowel 95.71% 94.29% 92.86% 92.86% 93.93% Word 90.00% 87.14% 90.00% 92.86% 90.00% Table I: 4 – Fold Cross-validation Result. So, Vowel Recognition outperforms Word Recognition in all cases. MD. RAKIBUL HASAN, INVESTIGATION OF THE EFFECT OF MFCC VARIATION ON THE CNN-BASED SPEECH CLASSIFICATION MULTIMEDIA AND SIGNAL PROCESSING (II)
- 10. Results & Discussion (Contd.) 10 What’s the reason behind the Performance Deviation? Vowel Word Fig. 4: MFCC variation. MD. RAKIBUL HASAN, INVESTIGATION OF THE EFFECT OF MFCC VARIATION ON THE CNN-BASED SPEECH CLASSIFICATION MULTIMEDIA AND SIGNAL PROCESSING (II)
- 11. Results & Discussion (Contd.) 11 What’s the reason behind the Performance Deviation? Variation of Vocal Tract Dynamics is the answer! ‘অ’ vowel ‘ব োতল’ word Fig. 5: Waveshape of the speech token. MD. RAKIBUL HASAN, INVESTIGATION OF THE EFFECT OF MFCC VARIATION ON THE CNN-BASED SPEECH CLASSIFICATION MULTIMEDIA AND SIGNAL PROCESSING (II)
- 12. Contributions & Conclusion 12 𝑷𝒆𝒓𝒇𝒐𝒓𝒎𝒂𝒏𝒄𝒆 ∝ 𝟏 𝑽𝒂𝒓𝒊𝒂𝒕𝒊𝒐𝒏 Creation of Dataset • Raw Speech Data Collection • Make them Isolated Feature Engineering • MFCC Extraction • Variation among MFCC features Neural Network Model • Optimum selection of the hyperparameters Performance Evaluation • Performance Metrics • Comparison between Vowel and Word Recognition MD. RAKIBUL HASAN, INVESTIGATION OF THE EFFECT OF MFCC VARIATION ON THE CNN-BASED SPEECH CLASSIFICATION MULTIMEDIA AND SIGNAL PROCESSING (II)
- 13. Question & Answer Session 13 THANK YOU Any Question is warmly welcome... MD. RAKIBUL HASAN, INVESTIGATION OF THE EFFECT OF MFCC VARIATION ON THE CNN-BASED SPEECH CLASSIFICATION MULTIMEDIA AND SIGNAL PROCESSING (II)