Speech emotion recognition based on minimal voice quality features

Jacob, Agnès

doi:10.1109/iccsp.2016.7754275

Cited by 19 publications

(10 citation statements)

References 3 publications

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“…These algorithms belong to supervised learning methods, and have achieved certain success, but their recognition efficiency is not high. Therefore, how to improve the feature extraction efficiency, obtain potential features, and enhance the recognition rate are still a research field that needs to be further studied [24].…”

Section: Speech Feature Extractormentioning

confidence: 99%

A novel speech emotion recognition algorithm based on wavelet kernel sparse classifier in stacked deep auto-encoder model

Wei

Zhao

2019

Pers Ubiquit Comput

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Since the contextual information has an important impact on the speaker's emotional state, how to use emotion-related context information to conduct feature learning is a key problem. The existing speech emotion recognition algorithms achieve the relatively high recognition rate; these algorithms are not very good application to the real-life speech emotion recognition systems. Therefore, in order to address the abovementioned issues, a novel speech emotion recognition algorithm based on improved stacked kernel sparse deep model is proposed in this paper, which is based on auto-encoder, denoising auto-encoder, and sparse auto-encoder to improve the Chinese speech emotion recognition. The first layer of the structure uses a denoising autoencoder to learn a hidden feature with a larger dimension than the dimension of the input features, and the second layer employs a sparse auto-encoder to learn sparse features. Finally, a wavelet-kernel sparse SVM classifier is applied to classify the features. The proposed algorithm is evaluated on the testing dataset, which contains the speech emotion data of spontaneous, non-prototypical, and long-term. The experimental results show that the proposed algorithm outperforms the existing state-of-the-art algorithms in speech emotion recognition.

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Section: Speech Feature Extractormentioning

confidence: 99%

A novel speech emotion recognition algorithm based on wavelet kernel sparse classifier in stacked deep auto-encoder model

Wei

Zhao

2019

Pers Ubiquit Comput

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“…For f 0 in the range [200, 1000] Hz, k in the range [0, 0.4] and f mod in the range [5,10] Hz it was necessary to create a more complex model than the previous one. Shimmer depends on the modulation frequency, so a new transformation is necessary (the first one was the transformation from time domain to frequency domain in the spectrogram).…”

Section: Shimmer Approximation With F 0 K and F Mod Variablementioning

confidence: 99%

“…Shimmer value is associated to voice quality [2][3][4][5][6][7], state of mind [8][9][10][11][12][13], age [14] and gender [15] of people. There are many research works that use shimmer (among other measures) with goals ranging from pathologies detection [6,16,17] to the improvement of human-machine interfaces through the estimation of the intensionality of a spoken phrase [19].…”

Section: Introductionmentioning

confidence: 99%

“…Classifiers based on neural networks can be divided into two groups according to the type of input features, those using previously calculated acoustic measures [10,14] and those using raw audio [22,24,25] or spectral data [21-23, 28-31, 34, 35]. In [26] a hybrid approach is applied by adding shimmer and other measures to improve the recognition achieved with spectral data.…”

Section: Introductionmentioning

confidence: 99%

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Deep Neural Networks for Shimmer Approximation in Synthesized Audio Signal

García

Destéfanis

2018

Communications in Computer and Information Science

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Shimmer is a classical acoustic measure of the amplitude perturbation of a signal. This kind of variation in the human voice allow to characterize some properties, not only of the voice itself, but of the person who speaks. During the last years deep learning techniques have become the state of the art for recognition tasks on the voice. In this work the relationship between shimmer and deep neural networks is analyzed. A deep learning model is created. It is able to approximate shimmer value of a simple synthesized audio signal (stationary and without formants) taking the spectrogram as input feature. It is concluded firstly, that for this kind of synthesized signal, a neural network like the one we proposed can approximate shimmer, and secondly, that the convolution layers can be designed in order to preserve the information of shimmer and transmit it to the following layers.

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“…Among them, the MFCC is the most commonly used spectral feature for SER because of its characteristics being similar to the human ear. Sound quality features have a great influence on the emotional state expressed in speech, and it mainly includes sounds from breathing, brightness and formant [29]. Acoustic features are typically extracted in frames and enable emotional recognition through simple statistical analysis.…”

Section: Introductionmentioning

confidence: 99%

Recognize basic emotional statesin speech by machine learning techniques using mel-frequency cepstral coefficient features

Yang

Dey

Sherratt

et al. 2020

IFS

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AI based Speech Recognition has been widely used in the consumer field for control of smart home personal assistants, with many such devices on the market. Smart home assistants that could detect the user's emotion would improve the communication between a user and the assistant enabling the assistant to offer more productive feedback. Thus, the aim of this work is to analyze emotional states in speech and propose a suitable algorithm considering performance verses complexity for deployment in smart home devices. The four emotional speeches were selected from the Berlin Emotional Database (EMO-DB) as experimental data, 26 MFCC features were extracted from each type of emotional speech to identify the emotions of happiness, anger, sadness and neutrality. Then, speaker-independent experiments for our Speech emotion Recognition (SER) were conducted by using the Back Propagation Neural Network (BPNN), Extreme Learning Machine (ELM), Probabilistic Neural Network (PNN) and Support Vector Machine (SVM). Synthesizing the recognition accuracy and processing time, this work shows that the performance of SVM was the best among the four methods as a good candidate to be deployed for SER in smart home devices. SVM achieved an overall accuracy of 92.4% while offering low computational requirements when training and testing. We conclude that the MFCC features and the SVM classification models used in speaker-independent experiments are highly effective in the automatic prediction of emotion.

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Speech emotion recognition based on minimal voice quality features

Cited by 19 publications

References 3 publications

A novel speech emotion recognition algorithm based on wavelet kernel sparse classifier in stacked deep auto-encoder model

A novel speech emotion recognition algorithm based on wavelet kernel sparse classifier in stacked deep auto-encoder model

Deep Neural Networks for Shimmer Approximation in Synthesized Audio Signal

Recognize basic emotional statesin speech by machine learning techniques using mel-frequency cepstral coefficient features

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