With the recent developments of technology and the advances in artificial intelligence and machine learning techniques, it has become possible for the robot to understand and respond to voice as part of Human-Robot Interaction (HRI). The voice-based interface robot can recognize the speech information from humans so that it will be able to interact more naturally with its human counterpart in different environments. In this work, a review of the voice-based interface for HRI systems has been presented. The review focuses on voice-based perception in HRI systems from three facets, which are: feature extraction, dimensionality reduction, and semantic understanding. For feature extraction, numerous types of features have been reviewed in various domains, such as time, frequency, cepstral (i.e. implementing the inverse Fourier transform for the signal spectrum logarithm), and deep domains. For dimensionality reduction, subspace learning can be used to eliminate the redundancies of high-dimensional features by further processing extracted features to reflect their semantic information better. For semantic understanding, the aim is to infer from the extracted features the objects or human behaviors. Numerous types of semantic understanding have been reviewed, such as speech recognition, speaker recognition, speaker gender detection, speaker gender and age estimation, and speaker localization. Finally, some of the existing voice-based interface issues and recommendations for future works have been outlined.
Identifying the gender of a person and his age by way of speaking is considered a crucial task in computer vision. It is a very important and active research topic with many areas of application, such as identifying a person, trustworthiness, demographic analysis, safety and health knowledge, visual monitoring, and aging progress. Data matching is to identify the gender of the person and his age. Thus, the study touches on a review of many research papers from 2016 to 2022. At the heart of the topic, many systematic reviews of multimodal pedagogies in Age and Gender Estimation for Adaptive were undertaken. However, no current study of the theme concerns connected to multimodal pedagogies in Age and Gender Estimation for Adaptive Learning has been published. The multimodal pedagogies in four different databases within the keywords indicate the heart of the topic. A qualitative thematic analysis based on 48 articles found during the search revealed four common themes, such as multimodal engagement and speech with the Human-Robot Interaction life world. The study touches on the presentation of many major concepts, namely Age Estimation, Gender Estimation, Speaker Recognition, Speech recognition, Speaker Localization, and Speaker Gender Identification. According to specific criteria, they were presented to all studies. The essay compares these themes to the thematic findings of other review studies on the same topic such as multimodal age, gender estimation, and dataset used. The main objective of this paper is to provide a comprehensive analysis based on the surveyed region. The study provides a platform for professors, researchers, and students alike, and proposes directions for future research.
Recently, age estimates from speech have received growing interest as they are important for many applications like custom call routing, targeted marketing, or user-profiling. In this work, an automatic system to estimate age in short speech utterances without depending on the text is proposed. From each utterance frame, four groups of features are extracted and then 10 statistical functionals are measured for each extracted dimension of the features, to be followed by dimensionality reduction using Linear Discriminant Analysis (LDA). Finally, bidirectional Gated-Recurrent Neural Networks (G-RNNs) are used to predict speaker age. Experiments are conducted on the VoxCeleb1 dataset to show the performance of the proposed system, which is the first attempt to do so for such a system. In gender-dependent system, the Mean Absolute Error (MAE) of the proposed system is 9.25 years, and 10.33 years, the Root Mean Square Error (RMSE) is 13.17 and 13.26, respectively, for female and male speakers. In gender_ independent system, the MAE of the proposed system is 10.96 years, and the RMSE is 15.47. The results show that the proposed system has a good performance on short-duration utterances, taking into consideration the high noise ratio in the VoxCeleb1 dataset.
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