Research on Different Feature Parameters in Speaker Recognition

Liu, Qiyue; Yao, Mingqiu; Han, XW; Wang, Fang

doi:10.4236/jsip.2013.42014

Cited by 10 publications

(3 citation statements)

References 1 publication

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“…Although each person's different vocal structure lead to different fundamental frequency, because of the pitch frequency's scope is a little small, the gap between different people is little, and the most important is pitch frequency is affected by a lot of factors, such as emotion, tone, it is very difficult to achieve accurate fundamental frequency. The male fundamental frequency is generally lower than the female [16]. …”

Section: Analysis Of Pitchmentioning

confidence: 92%

Analysis of Variations in Speech in Different Age Groups using Prosody Technique

Landge¹,

Deshmukh²,

Shrishrimal³

2015

IJCA

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Section: Analysis Of Pitchmentioning

confidence: 92%

Analysis of Variations in Speech in Different Age Groups using Prosody Technique

Landge¹,

Deshmukh²,

Shrishrimal³

2015

IJCA

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“…In comparison, un-supervised solutions to the speaker count problem generally use statistical methods for estimating the number of speakers in an open-set scenario as no prior knowledge of the speaker is available. The characteristics of a speaker's voice are reflected by the channel and glottal features [17]. Research suggests that the speaker-recognition systems depend on the spectral features extracted from very short time segments (sec frame) of a speech, formally known as MFCC [12].…”

Section: Related Workmentioning

confidence: 99%

A Distributed Approach to Speaker Count Problem in an Open-Set Scenario by Clustering Pitch Features

Pandey

Banerjee

2021

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Counting the number of speakers in an audio sample can lead to innovative applications, such as a real-time ranking system. Researchers have studied advanced machine learning approaches for solving the speaker count problem. However, these solutions are not efficient in real-time environments, as it requires pre-processing of a finite set of data samples. Another approach for solving the problem is via unsupervised learning or by using audio processing techniques. The research in this category is limited and does not consider the large-scale open set environment. In this paper, we propose a distributed clustering approach to address the speaker count problem. The separability of the speaker is computed using statistical pitch parameters. The proposed solution uses multiple microphones available in smartphones in a large geographical area to capture and extract statistical pitch features from the audio samples. These features are shared between the nodes to estimate the number of speakers in the neighborhood. One of the major challenges is to reduce the error count that arises due to the proximity of the users and multiple microphones. We evaluate the algorithm’s performance using real smartphones in a multi-group arrangement by capturing parallel conversations between the users in both indoor and outdoor scenarios. The average error count distance is 1.667 in a multi-group scenario. The average error count distances in indoor environments are 16% which is better than in the outdoor environment.

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“…In [6] the author presented the function of various features like pitch, formant, MFCC in the view of increasing the rate of identification. Pitch extraction was done using the converter technique that transforms the signal into cepstrum domain and used the homo-morphic analytical method for eliminating the channel impact.…”

Section: Related Workmentioning

confidence: 99%

Speech Feature Extraction for Gender Recognition

Pahwa¹,

Aggarwal²

2016

IJIGSP

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Speech Recognition Technology can be embedded in various real time applications in order to increase the human-computer interaction. From robotics to health care and aerospace, from interactive voice response systems to mobile telephony and telematics, speech recognition technology have enhanced the humanmachine interaction. Gender recognition is an important component for the application embedding speech recognition as it reduces the computational complexity for the further processing in these applications. The paper involves the extraction of one of the most dominant and most researched up on speech feature, Mel coefficients and its first and second order derivatives. We extracted 13 values for each of these from a data-set 46 speech samples containing the Hindi vowels (आ, इ, ई, उ, ऊ, ऋ, ए, ऎ, ऒ, ऑ) and trained them using a combined model of SVM and neural network classification to determine their gender using stacking. The results obtained showed the accuracy of 93.48% after taking into consideration the first Mel coefficient. The purpose of this study was to extract the correct features and to compare the performance based on first Mel coefficient.

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Research on Different Feature Parameters in Speaker Recognition

Cited by 10 publications

References 1 publication

Analysis of Variations in Speech in Different Age Groups using Prosody Technique

Analysis of Variations in Speech in Different Age Groups using Prosody Technique

A Distributed Approach to Speaker Count Problem in an Open-Set Scenario by Clustering Pitch Features

Speech Feature Extraction for Gender Recognition

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