Text-Independent Speaker Identification Using Vowel Formants

Abstract

Automatic speaker identification has become a challenging research problem due to its wide variety of applications. Neural networks and audio-visual identification systems can be very powerful, but they have limitations related to the number of speakers. The performance drops gradually as more and more users are registered with the system. This paper proposes a scalable algorithm for real-time text-independent speaker identification based on vowel recognition. Vowel formants are unique across different speakers and reflect the vocal tract information of a particular speaker. The contribution of this paper is the design of a scalable system based on vowel formant filters and a scoring scheme for classification of an unseen instance. Mel-Frequency Cepstral Coefficients (MFCC) and Linear Predictive Coding (LPC) have both been analysed for comparison to extract vowel formants by windowing the given signal. All formants are filtered by known formant frequencies to separate the vowel formants for further processing. The formant frequencies of each speaker are collected during the training phase. A test signal is also processed in the same way to find vowel formants and compare them with the saved vowel formants to identify the speaker for the current signal. A score-based scheme allows the speaker with the highest matching formants to own the current signal. This model requires less than 100 bytes of data to be saved for each speaker to be identified, and can identify the speaker within a second. Tests conducted on multiple databases show that this score-based scheme outperforms the back propagation neural network and Gaussian mixture models. Usually, the longer the speech files, the more significant were the improvements in accuracy.

Adjustable deterministic pseudonymization of speech

“…This study aims at hiding the global and inherent features of speakers, i.e., the vocal tract related spectral features (cf. Almaadeed et al (2016)) and some learned features, i.e., pitch and speaking rate. This translates to making changes in speech that relate to vocal tract length, average formant frequencies and intensities, pitch, and speaking rate.…”

Development of Acoustic Nonverbal Information Estimation System for Unconstrained Long-Term Monitoring of Daily Office Activity

“…One method of identifying a speaker on the basis of only acoustic information is to use individual variations in voice [35] such as in Mel-frequency cepstral coefficients (MFCC) [36]- [38] and linear predictive coding [37], [39] along with vector quantization [36], [39] or a Gaussian mixture model [36]- [39]. However, reliable speaker identification requires a certain speaking duration.…”

An Improved Approach for Text-Independent Speaker Recognition

“…For that, many features have been investigated in the literature [2], [11] where the cepstral features [6] are the most appropriate ones for speaker recognition tasks. Up today, the most popular and successful cepstral features are Mel frequency cepstral coefficients (MFCC) [3], [10], [22].…”