Use of Spectral Peaks in Autocorrelation and Group Delay Domains for Robust Speech Recognition

Farahani, Gholamreza; Ahadi, Seyed Mohammad; Homayounpoor, M.M.

doi:10.1109/icassp.2006.1660071

Cited by 6 publications

(8 citation statements)

References 14 publications

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“…being suitable for robust recognition applications. This feature has been used for automatic music retrieval (e.g., the well-known Shazam search engine by Wang [8]), but also for robust speech recognition (see Farahani et al [75]). …”

Section: Stft-based Frequency Featuresmentioning

confidence: 99%

A Review of Physical and Perceptual Feature Extraction Techniques for Speech, Music and Environmental Sounds

2016

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Endowing machines with sensing capabilities similar to those of humans is a prevalent quest in engineering and computer science. In the pursuit of making computers sense their surroundings, a huge effort has been conducted to allow machines and computers to acquire, process, analyze and understand their environment in a human-like way. Focusing on the sense of hearing, the ability of computers to sense their acoustic environment as humans do goes by the name of machine hearing. To achieve this ambitious aim, the representation of the audio signal is of paramount importance. In this paper, we present an up-to-date review of the most relevant audio feature extraction techniques developed to analyze the most usual audio signals: speech, music and environmental sounds. Besides revisiting classic approaches for completeness, we include the latest advances in the field based on new domains of analysis together with novel bio-inspired proposals. These approaches are described following a taxonomy that organizes them according to their physical or perceptual basis, being subsequently divided depending on the domain of computation (time, frequency, wavelet, image-based, cepstral, or other domains). The description of the approaches is accompanied with recent examples of their application to machine hearing related problems.

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Section: Stft-based Frequency Featuresmentioning

confidence: 99%

A Review of Physical and Perceptual Feature Extraction Techniques for Speech, Music and Environmental Sounds

2016

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“…15 displays the results obtained using MFCC, PAC (phase autocorrelation) and APP. Also, for comparison purposes, we have included the results of adding spectral peaks to feature vectors calculated using magnitude spectrum and called it TSP (threaded spectral peaks) (Strope & Alwan, 1998) and ACP (autocorrelation peaks) (Farahani et al, 2006a). According to Fig.…”

Section: Methodsmentioning

confidence: 99%

“…Due to the effectiveness of autocorrelation function in preserving peaks, we will also report the results of using the autocorrelation domain for extracting the first 3 formants of the speech signal (Strope & Alwan, 1998;Farahani et al, 2006a).…”

Section: Autocorrelation Peaks and Phase Features (App)mentioning

confidence: 99%

“…Since after the application of autocorrelation function, the spectral peaks become clearer, we expect the resultant feature vectors to be more robust to noise. As mentioned in (Strope & Alwan, 1998;Farahani et al, 2006a), the peaks of the speech spectrum are important for speech recognition. Hence, we decided to add three peak frequencies and two peak derivatives to the feature vector.…”

Section: Algorithm Implementationmentioning

confidence: 99%

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Autocorrelation-based Methods for Noise-Robust Speech Recognition

Farahani¹,

Ahadi²,

Homayounpour³

2007

Robust Speech Recognition and Understanding

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“…Also, it has been shown that the use of spectral peaks obtained from a filtered autocorrelation sequence can lead to a good performance under noisy conditions [12,13].…”

Section: Introductionmentioning

confidence: 99%

Robust Feature Extraction Using Autocorrelation Domain for Noisy Speech Recognition

Farahani¹

2017

SIPIJ

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Use of Spectral Peaks in Autocorrelation and Group Delay Domains for Robust Speech Recognition

Cited by 6 publications

References 14 publications

A Review of Physical and Perceptual Feature Extraction Techniques for Speech, Music and Environmental Sounds

A Review of Physical and Perceptual Feature Extraction Techniques for Speech, Music and Environmental Sounds

Autocorrelation-based Methods for Noise-Robust Speech Recognition

Robust Feature Extraction Using Autocorrelation Domain for Noisy Speech Recognition

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