Pitch-synchronous DCT features: A pilot study on speaker identification

TL;DR

This study introduces pitch-synchronous DCT features for speaker identification, demonstrating high accuracy and the potential to enhance existing methods by capturing discriminative speech information with minimal preprocessing.

Contribution

The paper presents a novel PS-DCT feature extraction method that directly utilizes voiced speech segments without preemphasis or windowing, improving speaker identification performance.

Findings

PS-DCT features achieve up to 90% accuracy with 16 codebook size.

Combined PS-DCT and MFCC features reach 100% accuracy.

DCT captures discriminative speech information with minimal preprocessing.

Abstract

We propose a new feature, namely, pitchsynchronous discrete cosine transform (PS-DCT), for the task of speaker identification. These features are obtained directly from the voiced segments of the speech signal, without any preemphasis or windowing. The feature vectors are vector quantized, to create one separate codebook for each speaker during training. The performance of the PS-DCT features is shown to be good, and hence it can be used to supplement other features for the speaker identification task. Speaker identification is also performed using Mel-frequency cepstral coefficient (MFCC) features and combined with the proposed features to improve its performance. For this pilot study, 30 speakers (14 female and 16 male) have been picked up randomly from the TIMIT database for the speaker identification task. On this data, both the proposed features and MFCC give an identification accuracy of 90% and 96.7% for codebook sizes of 16 and 32, respectively, and the combined features achieve 100% performance. Apart from the speaker identification task, this work also shows the capability of DCT to capture discriminative information from the speech signal with minimal pre-processing.