This article presents an exhaustive comparative investigation into the accuracy of gender identification across diverse geographical regions,employing a deep learning classification algorithm for speech signal analysi...This article presents an exhaustive comparative investigation into the accuracy of gender identification across diverse geographical regions,employing a deep learning classification algorithm for speech signal analysis.In this study,speech samples are categorized for both training and testing purposes based on their geographical origin.Category 1 comprises speech samples from speakers outside of India,whereas Category 2 comprises live-recorded speech samples from Indian speakers.Testing speech samples are likewise classified into four distinct sets,taking into consideration both geographical origin and the language spoken by the speakers.Significantly,the results indicate a noticeable difference in gender identification accuracy among speakers from different geographical areas.Indian speakers,utilizing 52 Hindi and 26 English phonemes in their speech,demonstrate a notably higher gender identification accuracy of 85.75%compared to those speakers who predominantly use 26 English phonemes in their conversations when the system is trained using speech samples from Indian speakers.The gender identification accuracy of the proposed model reaches 83.20%when the system is trained using speech samples from speakers outside of India.In the analysis of speech signals,Mel Frequency Cepstral Coefficients(MFCCs)serve as relevant features for the speech data.The deep learning classification algorithm utilized in this research is based on a Bidirectional Long Short-Term Memory(BiLSTM)architecture within a Recurrent Neural Network(RNN)model.展开更多
The performance of high density chips operating in the GHz range is mostly affected by on-chip interconnects. The interconnect delay depends on many factors, a few of them are inputs toggling patterns, line ＆ couplin...The performance of high density chips operating in the GHz range is mostly affected by on-chip interconnects. The interconnect delay depends on many factors, a few of them are inputs toggling patterns, line ＆ coupling parasitics, input rise/fall time and source/load characteristics. The transition time of the input is of prime importance in high speed circuits. This paper addresses the FDTD based analysis of transition time effects on functional and dynamic crosstalk. The analysis is carried out for equal and unequal transition times of coupled inputs. The analysis of the effects of unequal rise time is equally important because practically, it is quite common to have mismatching in the rise time of the signals transmitting through different length wires. To demonstrate the effects, two distributed RLC lines coupled inductively and capacitively are taken into consideration. The FDTD technique is used because it gives accurate results and carries time domain analysis of coupled lines. The number of lumps in SPICE simulations is considered the same as those of spatial segments. To validate the FDTD computed results, SPICE simulations are run and results are compared. A good agreement of the computed results has been observed with respect to SPICE simulated results. An average error of less than 3.2% is observed in the computation of the performance parameters using the proposed method.展开更多
文摘This article presents an exhaustive comparative investigation into the accuracy of gender identification across diverse geographical regions,employing a deep learning classification algorithm for speech signal analysis.In this study,speech samples are categorized for both training and testing purposes based on their geographical origin.Category 1 comprises speech samples from speakers outside of India,whereas Category 2 comprises live-recorded speech samples from Indian speakers.Testing speech samples are likewise classified into four distinct sets,taking into consideration both geographical origin and the language spoken by the speakers.Significantly,the results indicate a noticeable difference in gender identification accuracy among speakers from different geographical areas.Indian speakers,utilizing 52 Hindi and 26 English phonemes in their speech,demonstrate a notably higher gender identification accuracy of 85.75%compared to those speakers who predominantly use 26 English phonemes in their conversations when the system is trained using speech samples from Indian speakers.The gender identification accuracy of the proposed model reaches 83.20%when the system is trained using speech samples from speakers outside of India.In the analysis of speech signals,Mel Frequency Cepstral Coefficients(MFCCs)serve as relevant features for the speech data.The deep learning classification algorithm utilized in this research is based on a Bidirectional Long Short-Term Memory(BiLSTM)architecture within a Recurrent Neural Network(RNN)model.
文摘The performance of high density chips operating in the GHz range is mostly affected by on-chip interconnects. The interconnect delay depends on many factors, a few of them are inputs toggling patterns, line ＆ coupling parasitics, input rise/fall time and source/load characteristics. The transition time of the input is of prime importance in high speed circuits. This paper addresses the FDTD based analysis of transition time effects on functional and dynamic crosstalk. The analysis is carried out for equal and unequal transition times of coupled inputs. The analysis of the effects of unequal rise time is equally important because practically, it is quite common to have mismatching in the rise time of the signals transmitting through different length wires. To demonstrate the effects, two distributed RLC lines coupled inductively and capacitively are taken into consideration. The FDTD technique is used because it gives accurate results and carries time domain analysis of coupled lines. The number of lumps in SPICE simulations is considered the same as those of spatial segments. To validate the FDTD computed results, SPICE simulations are run and results are compared. A good agreement of the computed results has been observed with respect to SPICE simulated results. An average error of less than 3.2% is observed in the computation of the performance parameters using the proposed method.
