Emotion pitch variation analysis in Malay and English voice samples

Razak, Aishah Abdul; Abidin, Mohamad Izani Zainal; Komiya, R.

doi:10.1109/apcc.2003.1274322

Cited by 14 publications

(15 citation statements)

References 14 publications

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“…It is known that duration of phonemes is typically shorter in speech, than in music [15]. Furthermore, the speaker's pitch can be in the range between 50 and 400 Hz, and can vary for 160 Hz, especially if the speaker is surprised or excited [14]. On that basis speech harmonics go through more dramatic changes in speech acoustic class, than in music.…”

Section: A Mel-frequency Cepstral Coefficients Variancementioning

confidence: 98%

On-Line Speech/Music Segmentation for Broadcast News Domain

Kos

Grasic

Vlaj

et al. 2009

2009 16th International Conference on Systems, Signals and Image Processing

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This paper presents novel feature-group for on-line speech/music segmentation for broadcast news domain. The features are based on Mel-Frequency Cepstral Coefficients Variance (MFCCV). The idea behind the feature-group construction is the energy variation in a narrow frequency sub-band. The variation is bigger for speech than for music. For feature discrimination and segmentation ability evaluation the radio broadcast database was used. Results show that MFCCV features perform better than the classic MFCC features. The MFCCV features are very convenient speech/music discriminator for automatic speech recognition system where MFCC features are used, as they perform better than classic MFCC features and only one additional calculation step is needed.

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Section: A Mel-frequency Cepstral Coefficients Variancementioning

confidence: 98%

On-Line Speech/Music Segmentation for Broadcast News Domain

Kos

Grasic

Vlaj

et al. 2009

2009 16th International Conference on Systems, Signals and Image Processing

View full text Add to dashboard Cite

show abstract

“…Simulated databases consider emotional speech that has been recorded from professional actors, whereas elicited databases consist of speech emotion induced by amateur persons. Natural produced speech is difficult to analyze for several reasons [23], [29]. First, the recording of spontaneous speech is not free of background noises.…”

Section: Data Collectionmentioning

confidence: 99%

“…Furthermore, several studies have examined the effect of emotional content of first and second languages on prosodic features showing that these features tend to differ from one language to another [22]. Razak et al [23] found that Malay utterances showed slightly higher average pitch for emotions such as anger, happiness and surprise than English utterances, but were slightly lower in pitch than English utterances for the emotions of sadness and fear. Graham [24] found that Japanese speakers used higher pitch when speaking Japanese than when speaking English.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Linguistic and Prosodic Features of Bilingual Arabic–English Speakers for Speech Emotion Recognition

2020

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Speech emotion recognition (SER) research has usually focused on the analysis of the native language of speakers, most commonly, targeting European and Asian languages. In the present study, a bilingual Arabic/English speech emotion database elicited from 16 male and 16 female Egyptian participants was created in order to investigate how the linguistic and prosodic features were affected by the anger, fear, happiness and sadness emotions across Arabic and English emotional speech. The results of the linguistic analysis indicated that the participants preferred to express their emotions indirectly, mainly using religious references, and that the female participants tended to use language that was more tentative and emotionally expressive, while the male participants tended to use language that was more assertive and independent. As for the prosodic analysis, statistical t-tests showed that the prosodic features of pitch, intensity and speech rate were more indicative of anger and happiness while less relevant to fear and scarcely significant for sadness. Furthermore, speech emotion recognition performed using linear support vector machine (SVM) with AdaBoost also supported these results. In regard to first and second language linguistic features, there was no significant difference in the choice of words and structures expressing the different emotions in the two languages, but in terms of prosodic features, the females' speech showed higher pitch in Arabic in all cases while both genders showed close intensity values in the two languages and faster speech rate in Arabic than in English. INDEX TERMS Arabic speech analysis, bilingual linguistic and prosodic features, classification, speech emotion recognition. LAMIAA ABDEL-HAMID received the Ph.D. degree (Hons.

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“…As can be seen in Figure 1, spectral representations of speech and music can be very different, despite the fact that there is a human voice present in both cases. It is typical for speech that the speaker's pitch can have values between 50 Hz and 400 Hz, and can vary by as much as 160 Hz, especially if the speaker is excited or surprised [33,34]. Also, the duration of the phonemes is shorter for speech (40-200 milliseconds) than for the singing voice (600-1200 milliseconds) [35].…”

Section: Variance Mean Of Filter Bank Energymentioning

confidence: 99%

Online Speech/Music Segmentation Based on the Variance Mean of Filter Bank Energy

Kos

Grasic

Kačič

2009

EURASIP J. Adv. Signal Process.

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This paper presents a novel feature for online speech/music segmentation based on the variance mean of filter bank energy (VMFBE). The idea that encouraged the feature's construction is energy variation in a narrow frequency sub-band. The energy varies more rapidly, and to a greater extent for speech than for music. Therefore, an energy variance in such a sub-band is greater for speech than for music. The radio broadcast database and the BNSI broadcast news database were used for feature discrimination and segmentation ability evaluation. The calculation procedure of the VMFBE feature has 4 out of 6 steps in common with the MFCC feature calculation procedure. Therefore, it is a very convenient speech/music discriminator for use in real-time automatic speech recognition systems based on MFCC features, because valuable processing time can be saved, and computation load is only slightly increased. Analysis of the feature's speech/music discriminative ability shows an average error rate below 10% for radio broadcast material and it outperforms other features used for comparison, by more than 8%. The proposed feature as a standalone speech/music discriminator in a segmentation system achieves an overall accuracy of over 94% on radio broadcast material.

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Emotion pitch variation analysis in Malay and English voice samples

Cited by 14 publications

References 14 publications

On-Line Speech/Music Segmentation for Broadcast News Domain

On-Line Speech/Music Segmentation for Broadcast News Domain

Analysis of Linguistic and Prosodic Features of Bilingual Arabic–English Speakers for Speech Emotion Recognition

Online Speech/Music Segmentation Based on the Variance Mean of Filter Bank Energy

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