Excitation Features of Speech for Emotion Recognition Using Neutral Speech as Reference

Kadiri, Sudarsana Reddy; Gangamohan, P.; Gangashetty, Suryakanth V.; Alku, Paavo; Yegnanarayana, B.

doi:10.1007/s00034-020-01377-y

Cited by 21 publications

(8 citation statements)

References 52 publications

(108 reference statements)

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“…The different methods are well established to describe the vocal tract filter, but the researchers showed less interest in excitation features [12]. The study of [13] demonstrated the methods to capture the excitation features effectively and mentioned the future scope to combine excitation and vocal tract features. This motivates us to combine the derived excitation features from the LP Residual method and vocal tract filter characteristics.…”

Section: Rationale Behind Using Excitation Features In Speaker Identi...mentioning

confidence: 99%

“…Various approaches are used to extract excitation information [17,18]. The authors of [13,17] elaborated on the three excitation features based on strength, energy and derived frequency around Glottal Closure Instant (GCI) locations. The authors [17] concluded that the performance of recognition systems might be improved by combining excitation-based features with the features that describe the vocal tract systems.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This peak is an important excitation to the vocal tract. LP analysis derives the excitation component from the speech signal [13]. Due to the abrupt variations in the movements of the vocal fold, the impulse-like event is produced which gives the prominent characteristic of the excitation [13].…”

Section: Proposed Excitation Featuresmentioning

confidence: 99%

See 2 more Smart Citations

Enhancement in Speaker Identification through Feature Fusion using Advanced Dilated Convolution Neural Network

Pentapati

Sridevi

2023

Int. j. electr. comput. eng. syst. (Online)

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There are various challenges in identifying the speakers accurately. The Extraction of discriminative features is a vital task for accurate identification in the speaker identification task. Nowadays, speaker identification is widely investigated using deep learning. The complex and noisy speech data affects the performance of Mel Frequency Cepstral Coefficients (MFCC); hence, MFCC fails to represent the speaker characteristics accurately. In this proposed work, a novel text-independent speaker identification system is developed to enhance the performance by fusion of Log-MelSpectrum and excitation features. The excitation information is obtained due to the vibration of vocal folds, and it is represented using Linear Prediction (LP) residual. The various types of features extracted from the excitation are residual phase, sharpness, Energy of Excitation (EoE), and Strength of Excitation (SoE). The extracted features were processed with the dilated convolution neural network (dilated CNN) to fulfill the identification task. The extensive evaluation showed that the fusion of excitation features gives better results than the existing methods. The accuracy reaches 94.12% for 11 complex classes and 91.34% for 80 speakers, and Equal Error Rate (EER) is reduced to 1.16% for the proposed model. The proposed model is tested with the Librispeech corpus using Matlab 2021b tool, outperforming the existing baseline models. The proposed model achieves an accuracy improvement of 1.34% compared to the baseline system.

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Section: Rationale Behind Using Excitation Features In Speaker Identi...mentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Enhancement in Speaker Identification through Feature Fusion using Advanced Dilated Convolution Neural Network

Pentapati

Sridevi

2023

Int. j. electr. comput. eng. syst. (Online)

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“…Gangamohan et al [18] examined four excitation features (F0, the SoE, the EoE, and loudness) for discriminating different emotions. These features were used to build emotion detection [206] and emotion recognition systems [19], [20], [205]. The effectiveness of excitation during the production of emotional speech was examined in [207]- [209] using prosody modification to convert neutral speech to emotional speech.…”

Section: B Study Of Vocal Emotionsmentioning

confidence: 99%

Extraction and Utilization of Excitation Information of Speech: A Review

2021

Self Cite

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| Speech production can be regarded as a process where a time-varying vocal tract system (filter) is excited by a time-varying excitation. In addition to its linguistic message, the speech signal also carries information about, for example, the gender and age of the speaker. Moreover, the speech signal includes acoustical cues about several speaker traits, such as the emotional state and the state of health of the speaker. In order to understand the production of these acoustical cues by the human speech production mechanism and utilize this information in speech technology, it is necessary to extract features describing both the excitation and the filter of the human speech production mechanism. While the methods to estimate and parameterize the vocal tract system are well established, the excitation appears less studied. This article provides a review of signal processing approaches used for the extraction of excitation information from speech. This article highlights the importance of excitation information in the analysis and classification of phonation type and vocal emotions, in the analysis of nonverbal laughter sounds, and in studying pathological voices. Furthermore, recent developments of deep learning techniques in the context of extraction and utilization of the excitation information are discussed.

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“…Recognizing whether a person has a common cold from their speech can be beneficial for speech signal processing tasks. Speech recognition [ 27 ], speaker verification [ 38 ], and emotion detection systems [ 14 , 24 ] are trained on normal speech, so their performance is negatively affected when tested on cold speech. Analysis of cold speech can make these systems more robust.…”

Section: Introductionmentioning

confidence: 99%

Detection of Common Cold from Speech Signals using Deep Neural Network

Deb

Warule

Nair

et al. 2022

Circuits Syst Signal Process

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This paper presents a deep learning-based analysis and classification of cold speech observed when a person is diagnosed with the common cold. The common cold is a viral infectious disease that affects the throat and the nose. Since speech is produced by the vocal tract after linear filtering of excitation source information, during a common cold, its attributes are impacted by the throat and the nose. The proposed study attempts to develop a deep learning-based classification model that can accurately predict whether a person has a cold or not based on their speech. The common cold-related information is captured using Mel-frequency cepstral coefficients (MFCC) and linear predictive coding (LPC) from the speech signal. The data imbalance is handled using the sampling strategy, SMOTE–Tomek links. Then, utilizing MFCC and LPC features, a deep learning-based model is trained and then used to categorize cold speech. The performance of a deep learning-based method is compared to logistic regression, random forest, and gradient boosted tree classifiers. The proposed model is less complex and uses a smaller feature set while giving comparable results to other state-of-the-art methods. The proposed method gives an UAR of , higher than the benchmark OpenSMILE SVM result of . The study’s success will yield a noninvasive method for cold detection, which can further be extended to detect other speech-affecting pathologies.

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Excitation Features of Speech for Emotion Recognition Using Neutral Speech as Reference

Cited by 21 publications

References 52 publications

Enhancement in Speaker Identification through Feature Fusion using Advanced Dilated Convolution Neural Network

Enhancement in Speaker Identification through Feature Fusion using Advanced Dilated Convolution Neural Network

Extraction and Utilization of Excitation Information of Speech: A Review

Detection of Common Cold from Speech Signals using Deep Neural Network

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