Generative emotional AI for speech emotion recognition: The case for synthetic emotional speech augmentation

Latif, Siddique; Shahid, Abdullah; Qadir, Junaid

doi:10.1016/j.apacoust.2023.109425

Cited by 11 publications

(2 citation statements)

References 24 publications

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“…Özseven [54] discussed the effectiveness of the spectrogram as a time-frequency domain image in urban sound classification. Latif et al [55], Shafik et al [56] and Mushtaq et al [57] used the spectrogram as an effective acoustic feature in speech emotion recognition, speaker identification, and environmental sound classification, respectively. All of these approaches were based on deep learning.…”

Section: Literature Reviewmentioning

confidence: 99%

A Feature-Reduction Scheme Based on a Two-Sample t-Test to Eliminate Useless Spectrogram Frequency Bands in Acoustic Event Detection Systems

Hajihashemi,

Gharahbagh,

Hajaboutalebi

et al. 2024

Electronics

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Acoustic event detection (AED) systems, combined with video surveillance systems, can enhance urban security and safety by automatically detecting incidents, supporting the smart city concept. AED systems mostly use mel spectrograms as a well-known effective acoustic feature. The spectrogram is a combination of frequency bands. A big challenge is that some of the spectrogram bands may be similar in different events and be useless in AED. Removing useless bands reduces the input feature dimension and is highly desirable. This article proposes a mathematical feature analysis method to identify and eliminate ineffective spectrogram bands and improve AED systems’ efficiency. The proposed approach uses a Student’s t-test to compare frequency bands of the spectrogram from different acoustic events. The similarity between each frequency band among events is calculated using a two-sample t-test, allowing the identification of distinct and similar frequency bands. Removing these bands accelerates the training speed of the used classifier by reducing the number of features, and also enhances the system’s accuracy and efficiency. Based on the obtained results, the proposed method reduces the spectrogram bands by 26.3%. The results showed an average difference of 7.77% in the Jaccard, 4.07% in the Dice, and 5.7% in the Hamming distance between selected bands using train and test datasets. These small values underscore the validity of the obtained results for the test dataset.

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Section: Literature Reviewmentioning

confidence: 99%

A Feature-Reduction Scheme Based on a Two-Sample t-Test to Eliminate Useless Spectrogram Frequency Bands in Acoustic Event Detection Systems

Hajihashemi,

Gharahbagh,

Hajaboutalebi

et al. 2024

Electronics

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show abstract

“…In [7] The paper proposes an end-to-end multi-speaker emotional text-to-speech system with a condition encoder to modulate speaker voice features and emotions in the output. It then utilizes the synthesized emotional speech to augment speech emotion recognition systems, showing improvements in performance.…”

Section: Literature Surveymentioning

confidence: 99%

Speech Emotion Recognition Using ANN

2024

IJSREM

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The speech is the most effective means of communication, to recognize the emotions in speech is the most crucial task. In this paper we are using the Artificial Neural Network to recognize the emotions in speech. Hence, providing an efficient and accurate technique for speech based emotion recognition is also an important task. This study is focused on seven basic human emotions (angry, disgust, fear, happy, neutral, surprise, sad). The training and validating accuracy and also lose can be seen in a graph while training the dataset. According to it confusion matrix for model is created. The seven features Frequency, Pitch, Amplitude and formant of speech is used to recognize seven basic emotions from speech. Keywords: Emotion detection, Emotion classification, verbal emotion classification.

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Self-supervised representation learning anomaly detection methodology based on boosting algorithms enhanced by data augmentation using StyleGAN for manufacturing imbalanced data

Kim,

Lee,

Hyun

et al. 2023

Computers in Industry

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Generative emotional AI for speech emotion recognition: The case for synthetic emotional speech augmentation

Cited by 11 publications

References 24 publications

A Feature-Reduction Scheme Based on a Two-Sample t-Test to Eliminate Useless Spectrogram Frequency Bands in Acoustic Event Detection Systems

A Feature-Reduction Scheme Based on a Two-Sample t-Test to Eliminate Useless Spectrogram Frequency Bands in Acoustic Event Detection Systems

Speech Emotion Recognition Using ANN

Self-supervised representation learning anomaly detection methodology based on boosting algorithms enhanced by data augmentation using StyleGAN for manufacturing imbalanced data

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