Improvement of speech recognition results by a combination of systems

Rama Hasan; Hussein Hussein; Pavlos Lazaridis; Sinan Khwandah; Marc Ritter; Maximilian Eibl

doi:10.23919/IConAC.2017.8082082

Improvement of speech recognition results by a combination of systems

Rama Hasan, Hussein Hussein, Pavlos Lazaridis, Sinan Khwandah, Marc Ritter, Maximilian Eibl

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

Abstract

The aim of this study is to suggest an algorithm that combines two speech recognition systems. These systems differ in the methods used in the feature extraction stage, but they have the same classifier Hidden Markov Model (HMM). The first system uses Mel-Frequency Cepstrum Coefficients (MFCC), the second one uses Linear Prediction Cepstrum Coefficients (LPCC), and the third system uses Perceptual Linear Predictive (PLP) features. The combination algorithm is applied separately on each couple of systems. The study is implemented on a data set that consists of the four voice commands: 'shutdown', 'documents', 'restart', and 'net' pronounced by 33 people. In addition to the improvement of the speech recognition rate for isolated words, the study aimed to determine the most complementary couple of systems through studying two kinds of errors: simultaneous and dependent errors. The system depending on MFCC features provided the highest recognition rate with 85.44%. The results showed noticeable improvement of combined systems in comparison with the individual systems where combining MFCC & PLP provided the highest recognition rate with 93.44%.

Original language	English
Title of host publication	2017 23rd IEEE International Conference on Automation and Computing (ICAC)
Subtitle of host publication	Addressing Global Challenges through Automation and Computing
Editors	Jie Zhang
Publisher	Institute of Electrical and Electronics Engineers Inc.
Number of pages	4
ISBN (Electronic)	9780701702601
DOIs	https://doi.org/10.23919/IConAC.2017.8082082
Publication status	Published - 26 Oct 2017
Event	23rd International Conference on Automation and Computing: Addressing Global Challenges through Automation and Computing - University of Huddersfield, Huddersfield, United Kingdom Duration: 7 Sep 2017 → 8 Sep 2017 Conference number: 23 https://www.ieee.org/conferences_events/conferences/conferencedetails/index.html?Conf_ID=41042 (Link to Conference Website)

Conference

Conference	23rd International Conference on Automation and Computing
Abbreviated title	ICAC 2017
Country/Territory	United Kingdom
City	Huddersfield
Period	7/09/17 → 8/09/17
Other	The scope of the conference covers a broad spectrum of areas with multi-disciplinary interests in the fields of automation, control engineering, computing and information systems, ranging from fundamental research to real-world applications.
Internet address	https://www.ieee.org/conferences_events/conferences/conferencedetails/index.html?Conf_ID=41042 (Link to Conference Website)

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Hasan, R., Hussein, H., Lazaridis, P., Khwandah, S., Ritter, M., & Eibl, M. (2017). Improvement of speech recognition results by a combination of systems. In J. Zhang (Ed.), 2017 23rd IEEE International Conference on Automation and Computing (ICAC): Addressing Global Challenges through Automation and Computing [8082082] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/IConAC.2017.8082082

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title = "Improvement of speech recognition results by a combination of systems",

abstract = "The aim of this study is to suggest an algorithm that combines two speech recognition systems. These systems differ in the methods used in the feature extraction stage, but they have the same classifier Hidden Markov Model (HMM). The first system uses Mel-Frequency Cepstrum Coefficients (MFCC), the second one uses Linear Prediction Cepstrum Coefficients (LPCC), and the third system uses Perceptual Linear Predictive (PLP) features. The combination algorithm is applied separately on each couple of systems. The study is implemented on a data set that consists of the four voice commands: 'shutdown', 'documents', 'restart', and 'net' pronounced by 33 people. In addition to the improvement of the speech recognition rate for isolated words, the study aimed to determine the most complementary couple of systems through studying two kinds of errors: simultaneous and dependent errors. The system depending on MFCC features provided the highest recognition rate with 85.44%. The results showed noticeable improvement of combined systems in comparison with the individual systems where combining MFCC & PLP provided the highest recognition rate with 93.44%.",

keywords = "Features extraction, Hidden Markov models, Speech recognition",

author = "Rama Hasan and Hussein Hussein and Pavlos Lazaridis and Sinan Khwandah and Marc Ritter and Maximilian Eibl",

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doi = "10.23919/IConAC.2017.8082082",

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note = "23rd International Conference on Automation and Computing : Addressing Global Challenges through Automation and Computing, ICAC 2017 ; Conference date: 07-09-2017 Through 08-09-2017",

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Hasan, R, Hussein, H, Lazaridis, P, Khwandah, S, Ritter, M & Eibl, M 2017, Improvement of speech recognition results by a combination of systems. in J Zhang (ed.), 2017 23rd IEEE International Conference on Automation and Computing (ICAC): Addressing Global Challenges through Automation and Computing., 8082082, Institute of Electrical and Electronics Engineers Inc., 23rd International Conference on Automation and Computing, Huddersfield, United Kingdom, 7/09/17. https://doi.org/10.23919/IConAC.2017.8082082

Improvement of speech recognition results by a combination of systems. / Hasan, Rama; Hussein, Hussein; Lazaridis, Pavlos et al.

2017 23rd IEEE International Conference on Automation and Computing (ICAC): Addressing Global Challenges through Automation and Computing. ed. / Jie Zhang. Institute of Electrical and Electronics Engineers Inc., 2017. 8082082.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Hasan, Rama

AU - Hussein, Hussein

AU - Lazaridis, Pavlos

AU - Khwandah, Sinan

AU - Ritter, Marc

AU - Eibl, Maximilian

N1 - Conference code: 23

PY - 2017/10/26

Y1 - 2017/10/26

N2 - The aim of this study is to suggest an algorithm that combines two speech recognition systems. These systems differ in the methods used in the feature extraction stage, but they have the same classifier Hidden Markov Model (HMM). The first system uses Mel-Frequency Cepstrum Coefficients (MFCC), the second one uses Linear Prediction Cepstrum Coefficients (LPCC), and the third system uses Perceptual Linear Predictive (PLP) features. The combination algorithm is applied separately on each couple of systems. The study is implemented on a data set that consists of the four voice commands: 'shutdown', 'documents', 'restart', and 'net' pronounced by 33 people. In addition to the improvement of the speech recognition rate for isolated words, the study aimed to determine the most complementary couple of systems through studying two kinds of errors: simultaneous and dependent errors. The system depending on MFCC features provided the highest recognition rate with 85.44%. The results showed noticeable improvement of combined systems in comparison with the individual systems where combining MFCC & PLP provided the highest recognition rate with 93.44%.

AB - The aim of this study is to suggest an algorithm that combines two speech recognition systems. These systems differ in the methods used in the feature extraction stage, but they have the same classifier Hidden Markov Model (HMM). The first system uses Mel-Frequency Cepstrum Coefficients (MFCC), the second one uses Linear Prediction Cepstrum Coefficients (LPCC), and the third system uses Perceptual Linear Predictive (PLP) features. The combination algorithm is applied separately on each couple of systems. The study is implemented on a data set that consists of the four voice commands: 'shutdown', 'documents', 'restart', and 'net' pronounced by 33 people. In addition to the improvement of the speech recognition rate for isolated words, the study aimed to determine the most complementary couple of systems through studying two kinds of errors: simultaneous and dependent errors. The system depending on MFCC features provided the highest recognition rate with 85.44%. The results showed noticeable improvement of combined systems in comparison with the individual systems where combining MFCC & PLP provided the highest recognition rate with 93.44%.

KW - Features extraction

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AN - SCOPUS:85039989988

BT - 2017 23rd IEEE International Conference on Automation and Computing (ICAC)

A2 - Zhang, Jie

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 23rd International Conference on Automation and Computing

Y2 - 7 September 2017 through 8 September 2017

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Hasan R, Hussein H, Lazaridis P, Khwandah S, Ritter M, Eibl M. Improvement of speech recognition results by a combination of systems. In Zhang J, editor, 2017 23rd IEEE International Conference on Automation and Computing (ICAC): Addressing Global Challenges through Automation and Computing. Institute of Electrical and Electronics Engineers Inc. 2017. 8082082 doi: 10.23919/IConAC.2017.8082082

Improvement of speech recognition results by a combination of systems

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