Age Estimation in Short Speech Utterances Based on LSTM Recurrent Neural Networks

Zazo, Rubén; Nidadavolu, Phani Sankar; Chen, Nanxin; González-Rodríguez, Joaquín; Dehak, Najim

doi:10.1109/access.2018.2816163

Cited by 70 publications

(33 citation statements)

References 21 publications

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“…Indeed, one can consider an alternative approach to attaching a face image to an input voice by first predicting some attributes from the person's voice (e.g., their age, gender, etc. [53]), and then either fetching an image from a database that best fits the predicted set of attributes, or using the attributes to generate an image [52]. However, this approach has several limitations.…”

Section: Introductionmentioning

confidence: 99%

Speech2Face: Learning the Face Behind a Voice

Oh¹,

Dekel

Kim³

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

138

108

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How much can we infer about a person's looks from the way they speak? In this paper, we study the task of reconstructing a facial image of a person from a short audio recording of that person speaking. We design and train a deep neural network to perform this task using millions of natural Internet/YouTube videos of people speaking. During training, our model learns voice-face correlations that allow it to produce images that capture various physical attributes of the speakers such as age, gender and ethnicity. This is done in a self-supervised manner, by utilizing the natural co-occurrence of faces and speech in Internet videos, without the need to model attributes explicitly. We evaluate and numerically quantify how-and in what manner-our Speech2Face reconstructions, obtained directly from audio, resemble the true face images of the speakers.

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Section: Introductionmentioning

confidence: 99%

Speech2Face: Learning the Face Behind a Voice

Oh¹,

Dekel

Kim³

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

138

108

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“…Finally, Experimental results show 28% of Mean Absolute Error (MAE) with LSTM and RNN. (Zazo, et al 2018) .…”

Section: Wa (Weightedmentioning

confidence: 99%

Speaker Recognition System based on Age-related Features using Convolutional and Deep Neural Networks

Kuppusamy

Chandra

2020

Preprint

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With the advent of conversational voice recognition systems growing such as Alexa, SIRI, OK Google, etc., natural language conversational systems including Chatbot and voice recognition systems are in new high and determining the age of a speaker is critical for setting the pertinent context. Age can be inferred from the speech signal by inferring various factors such as physical attributes of voice, linguistic attributes, frequency, speech rate,etc., The proposed research article discusses about extracting the spectral features of speech such as Cepstral Coefficients, Spectral Decrease, Centroid, Flatness, Spectral Entropy, F0DIFF, Jitter and Shimmer as inputs. This would help in classifying speaker age through deep learning techniques. A novel approach is addressed along with the model for implementation using Deep Neural Network and Convolutional Neural Network for classifying the features using three different classifiers which are Gaussian Mixture Model (GMM), Support Vector Machine (SVM) and GMM-SVM. The results obtained from the proposed system would outline the performance in speaker age recognition.

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“…The concept of the memory cell and gate was incorporated into the RNN. Figure 5 shows a single memory block of LSTM, (16) which includes three gates: Input, Output, and Forget. The gates are all nonlinear summing units.…”

Section: Lstmmentioning

confidence: 99%

Dual-input Control Interface for Deep Neural Network Based on Image/Speech Recognition

Pai¹,

Chen²,

Hung³

et al. 2019

Sensors and Materials

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The objective of this study was to design a control interface for dual-input video/audio recognition consisting of two input interface systems, hand posture and speech recognition, with the use of specific hand postures or voice commands for control without the need for wearable devices. Original video camera images were preprocessed for hand posture recognition, and the face in the image was used as the reference point and identified using the Adaboost classifier. An image of a specific size was selected as the recognition input image to increase the recognition speed. A neural network comprising convolutional, activation, max pooling, and fully connected layers was used to classify and recognize hand posture images as well as speech. Long short-term memory (LSTM) in a recurrent neural network (RNN) was used to achieve speech recognition. Speech features were extracted by preprocessing, and Melfrequency cepstral coefficients (MFCCs) and a fast Fourier transform (FFT) were then used to convert the signals from the time domain to the frequency domain. The frequency domain signals subsequently underwent a discrete cosine transform through triangular bandpass filters to derive MFCCs as the speech eigenvalue input. The speech feature parameters were then input to the LSTM neural network to make predictions and achieve speech recognition. Experimental results showed the image/speech dual-input control interface had good sound recognition capability, supporting the findings of this study.

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Age Estimation in Short Speech Utterances Based on LSTM Recurrent Neural Networks

Cited by 70 publications

References 21 publications

Speech2Face: Learning the Face Behind a Voice

Speech2Face: Learning the Face Behind a Voice

Speaker Recognition System based on Age-related Features using Convolutional and Deep Neural Networks

Dual-input Control Interface for Deep Neural Network Based on Image/Speech Recognition

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