Convolutional-recurrent Neural Network for Age and Gender Prediction from Speech

Sánchez-Hevia, Héctor A.; Gil-Pita, Roberto; Rosa-Zurera, Manuel

doi:10.1109/sps.2019.8881961

Cited by 11 publications

(10 citation statements)

References 10 publications

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“…However, emotion recognition through speech analysis is progressively gaining momentum [18], especially so in the context of health research [19]. Speech and language carry information about the speaker, including age and gender [20], as well as physiological, behavioural and emotional information [21]. Speech is ubiquitous and may be collected automatically, unobstrusively and with relatively little infrastructure.…”

Section: Related Workmentioning

confidence: 99%

COVID-19: Affect recognition through voice analysis during the winter lockdown in Scotland

Garcia

Haider

Luz

2021

Preprint

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The COVID-19 pandemic has led to unprecedented restrictions in people's lifestyle which have affected their psychological wellbeing. In this context, this paper investigates the use of social signal processing techniques for remote assessment of emotions. It presents a machine learning method for affect recognition applied to recordings taken during the COVID-19 winter lockdown in Scotland (UK). This method is exclusively based on acoustic features extracted from voice recordings collected through home and mobile devices (i.e. phones, tablets), thus providing insight into the feasibility of monitoring people's psychological wellbeing remotely, automatically and at scale. The proposed model is able to predict affect with a concordance correlation coefficient of 0.4230 (using Random Forest) and 0.3354 (using Decision Trees) for arousal and valence respectively. Clinical relevance: In 2018/2019, 12% and 14% of Scottish adults reported depression and anxiety symptoms. Remote emotion recognition through home devices would support the detection of these difficulties, which are often underdiagnosed and, if untreated, may lead to temporal or chronic disability.

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Section: Related Workmentioning

confidence: 99%

COVID-19: Affect recognition through voice analysis during the winter lockdown in Scotland

Garcia

Haider

Luz

2021

Preprint

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“…Gender identification is an easier problem with state-of-the art systems achieving error rates of around 2-3% [30]. In a previous work [32], we have proposed a system based on Convolutional-Recurrent neural networks implemented using the Keras library in Python, for gender distinction and age classification into three groups: youths, adults and elderly people. A mean error below 2% was reported for gender distinction, which is comparable to the best published in the literature.…”

Section: Introductionmentioning

confidence: 99%

“…This paper is an extended version of [33], with a more detailed analysis of the results. A preliminary study with recurrent neural networks demonstrated the usefulness of this approach [32].…”

Section: Introductionmentioning

confidence: 99%

Age group classification and gender recognition from speech with temporal convolutional neural networks

Sánchez-Hevia

Gil-Pita

Rosa-Zurera

2022

Multimed Tools Appl

Self Cite

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This paper analyses the performance of different types of Deep Neural Networks to jointly estimate age and identify gender from speech, to be applied in Interactive Voice Response systems available in call centres. Deep Neural Networks are used, because they have recently demonstrated discriminative and representation capabilities in a wide range of applications, including speech processing problems based on feature extraction and selection. Networks with different sizes are analysed to obtain information on how performance depends on the network architecture and the number of free parameters. The speech corpus used for the experiments is Mozilla’s Common Voice dataset, an open and crowdsourced speech corpus. The results are really good for gender classification, independently of the type of neural network, but improve with the network size. Regarding the classification by age groups, the combination of convolutional neural networks and temporal neural networks seems to be the best option among the analysed, and again, the larger the size of the network, the better the results. The results are promising for use in IVR systems, with the best systems achieving a gender identification error of less than 2% and a classification error by age group of less than 20%.

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“…Moreover, speech signals play an essential role in human–computer interaction (HCI). Nowadays, the speech signal is being used as a primary input source for several applications, such as automatic speech recognition (ASR) [ 1 ], speech emotion recognition (SER) [ 2 ], gender recognition, and age estimation [ 3 , 4 ]. Additionally, automatically extracting the age, gender, and emotional state of a speaker from speech signals has recently become an emerging field of study.…”

Section: Introductionmentioning

confidence: 99%

“…The authors of [ 8 ] proposed a novel age estimation system based on Long short-term memory (LSTM) recurrent neural networks (RNN) that can deal with short utterances using acoustic features. Another notable method was proposed, which utilized a convolutional recurrent neural network (CRNN) for age and gender prediction from speech utterances [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Age and Gender Recognition Using a Convolutional Neural Network with a Specially Designed Multi-Attention Module through Speech Spectrograms

Tursunov

Mustaqeem

Choeh

et al. 2021

Sensors

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Speech signals are being used as a primary input source in human–computer interaction (HCI) to develop several applications, such as automatic speech recognition (ASR), speech emotion recognition (SER), gender, and age recognition. Classifying speakers according to their age and gender is a challenging task in speech processing owing to the disability of the current methods of extracting salient high-level speech features and classification models. To address these problems, we introduce a novel end-to-end age and gender recognition convolutional neural network (CNN) with a specially designed multi-attention module (MAM) from speech signals. Our proposed model uses MAM to extract spatial and temporal salient features from the input data effectively. The MAM mechanism uses a rectangular shape filter as a kernel in convolution layers and comprises two separate time and frequency attention mechanisms. The time attention branch learns to detect temporal cues, whereas the frequency attention module extracts the most relevant features to the target by focusing on the spatial frequency features. The combination of the two extracted spatial and temporal features complements one another and provide high performance in terms of age and gender classification. The proposed age and gender classification system was tested using the Common Voice and locally developed Korean speech recognition datasets. Our suggested model achieved 96%, 73%, and 76% accuracy scores for gender, age, and age-gender classification, respectively, using the Common Voice dataset. The Korean speech recognition dataset results were 97%, 97%, and 90% for gender, age, and age-gender recognition, respectively. The prediction performance of our proposed model, which was obtained in the experiments, demonstrated the superiority and robustness of the tasks regarding age, gender, and age-gender recognition from speech signals.

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Convolutional-recurrent Neural Network for Age and Gender Prediction from Speech

Cited by 11 publications

References 10 publications

COVID-19: Affect recognition through voice analysis during the winter lockdown in Scotland

COVID-19: Affect recognition through voice analysis during the winter lockdown in Scotland

Age group classification and gender recognition from speech with temporal convolutional neural networks

Age and Gender Recognition Using a Convolutional Neural Network with a Specially Designed Multi-Attention Module through Speech Spectrograms

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