Adaptive-order fractional fourier transform features for speech recognition

Hui Yin; Xiang Xie; Jingming Kuang

Adaptive-order fractional fourier transform features for speech recognition

Hui Yin^*, Xiang Xie, Jingming Kuang

^*Corresponding author for this work

School of Information and Electronics

Beijing Institute of Technology

Research output: Contribution to journal › Conference article › peer-review

3 Citations (Scopus)

Abstract

We propose an acoustic feature for speech recognition based on the combination of MFCC and fractional Fourier transform (FrFT). Since the transform order is critical for the performance of FrFT, we use the ambiguity function to adaptively determine the optimal orders of FrFT for each frame. The performance of the proposed feature is compared with traditional MFCCs on recognizing speech of isolated and connected digits under both clean and noisy backgrounds. The recognition results and detailed confusion matrices are given and analyzed, which implies that the proposed feature is promising in certain speech processing fields.

Original language	English
Pages (from-to)	654-657
Number of pages	4
Journal	Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Publication status	Published - 2008
Event	INTERSPEECH 2008 - 9th Annual Conference of the International Speech Communication Association - Brisbane, QLD, Australia Duration: 22 Sept 2008 → 26 Sept 2008

Keywords

Ambiguity function
Feature extraction
Fractional fourier transform
Speech recognition

Cite this

@article{dc5382dcdd6045eb9dbf63c9b955e908,

title = "Adaptive-order fractional fourier transform features for speech recognition",

abstract = "We propose an acoustic feature for speech recognition based on the combination of MFCC and fractional Fourier transform (FrFT). Since the transform order is critical for the performance of FrFT, we use the ambiguity function to adaptively determine the optimal orders of FrFT for each frame. The performance of the proposed feature is compared with traditional MFCCs on recognizing speech of isolated and connected digits under both clean and noisy backgrounds. The recognition results and detailed confusion matrices are given and analyzed, which implies that the proposed feature is promising in certain speech processing fields.",

keywords = "Ambiguity function, Feature extraction, Fractional fourier transform, Speech recognition",

author = "Hui Yin and Xiang Xie and Jingming Kuang",

year = "2008",

language = "English",

pages = "654--657",

journal = "Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH",

issn = "2308-457X",

note = "INTERSPEECH 2008 - 9th Annual Conference of the International Speech Communication Association ; Conference date: 22-09-2008 Through 26-09-2008",

}

TY - JOUR

T1 - Adaptive-order fractional fourier transform features for speech recognition

AU - Yin, Hui

AU - Xie, Xiang

AU - Kuang, Jingming

PY - 2008

Y1 - 2008

N2 - We propose an acoustic feature for speech recognition based on the combination of MFCC and fractional Fourier transform (FrFT). Since the transform order is critical for the performance of FrFT, we use the ambiguity function to adaptively determine the optimal orders of FrFT for each frame. The performance of the proposed feature is compared with traditional MFCCs on recognizing speech of isolated and connected digits under both clean and noisy backgrounds. The recognition results and detailed confusion matrices are given and analyzed, which implies that the proposed feature is promising in certain speech processing fields.

AB - We propose an acoustic feature for speech recognition based on the combination of MFCC and fractional Fourier transform (FrFT). Since the transform order is critical for the performance of FrFT, we use the ambiguity function to adaptively determine the optimal orders of FrFT for each frame. The performance of the proposed feature is compared with traditional MFCCs on recognizing speech of isolated and connected digits under both clean and noisy backgrounds. The recognition results and detailed confusion matrices are given and analyzed, which implies that the proposed feature is promising in certain speech processing fields.

KW - Ambiguity function

KW - Feature extraction

KW - Fractional fourier transform

KW - Speech recognition

UR - http://www.scopus.com/inward/record.url?scp=77649246817&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:77649246817

SN - 2308-457X

SP - 654

EP - 657

JO - Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH

JF - Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH

T2 - INTERSPEECH 2008 - 9th Annual Conference of the International Speech Communication Association

Y2 - 22 September 2008 through 26 September 2008

ER -

Adaptive-order fractional fourier transform features for speech recognition

Abstract

Keywords

Other files and links

Fingerprint

Cite this