Memory and computation effective approaches for i-vector extraction

This paper focuses on the extraction of i-vectors, a compact representation of spoken utterances that is used by most of the state-of-the-art speaker recognition systems. This work was mainly motivated by the need of reducing the memory demand of the huge data structures that are usually precomputed for fast computation of the i-vectors. We propose a set of new approaches allowing accurate i-vector extraction but requiring less memory, showing their relations with the standard computation method introduced for eigenvoices. We analyze the time and memory resources required by these solutions, which are suited to different fields of application, and we show that it is possible to get accurate results with solutions that reduce both computation time and memory demand compared with the standard solutio

Text Dependent Speaker Verification menggunakan I-Vector Extraction dan GMM

Dibandingkan metode verifikasi identitas biometrik lain, speaker verification memiliki kelebihan yaitu telah banyaknya perangkat mikrofon tersemat pada berbagai perangkat. Hal tersebut tentu menarik karena memungkinkan untuk ditambahnya metode verifikasi ini melalui pembaruan perangkat lunak tanpa memerlukan perangkat keras lain. Penelitian mengenai speaker verification telah banyak dilakukan beriringan dengan penelitian speaker recognition lainnya. Speaker recognition biasanya menggunakan MFCC (Mel Frequency Cepstral Coefficients) untuk melakukan pengenalan suara. Dalam tugas akhir ini akan dilakukan pengetesan akurasi sebuah sistem Text-Dependent Speaker Verification (TD-SV) yang menggunakan i-vector extraction dan Gaussian Mixture Model (GMM). I-Vector extraction diketahui memiliki akurasi yang lebih baik pada aplikasi Speaker Recognition dibandingkan dengan MFCC. Penelitian ini dapat menunjukkan berapa besar akurasi TD-SV menggunakan i-vector extraction dan GMM. Mnggunakan i-vector extraction dan GMM, didapatkan False Rejection Rate sebesar 60%, False Acceptance Rate sebesar 0,00% dan Error Rate sebesar 12%. Kata Kunci : Text Dependent Speaker Recognition, I-vector, Gaussian Mixture Mode

Memory-aware i-vector extraction by means of subspace factorization

Most of the state–of–the–art speaker recognition systems use i– vectors, a compact representation of spoken utterances. Since the “standard” i–vector extraction procedure requires large memory structures, we recently presented the Factorized Sub-space Estimation (FSE) approach, an efficient technique that dramatically reduces the memory needs for i–vector extraction, and is also fast and accurate compared to other proposed approaches. FSE is based on the approximation of the matrix T, representing the speaker variability sub–space, by means of the product of appropriately designed matrices. In this work, we introduce and evaluate a further approximation of the matrices that most contribute to the memory costs in the FSE approach, showing that it is possible to obtain comparable system accuracy using less than a half of FSE memory, which corresponds to more than 60 times memory reduction with respect to the standard method of i–vector extraction

Memory and computation trade-offs for efficient i-vector extraction

This work aims at reducing the memory demand of the data structures that are usually pre-computed and stored for fast computation of the i-vectors, a compact representation of spoken utterances that is used by most state-of-the-art speaker recognition systems. We propose two new approaches allowing accurate i-vector extraction but requiring less memory, showing their relations with the standard computation method introduced for eigenvoices, and with the recently proposed fast eigen-decomposition technique. The first approach computes an i-vector in a Variational Bayes (VB) framework by iterating the estimation of one sub-block of i-vector elements at a time, keeping fixed all the others, and can obtain i-vectors as accurate as the ones obtained by the standard technique but requiring only 25% of its memory. The second technique is based on the Conjugate Gradient solution of a linear system, which is accurate and uses even less memory, but is slower than the VB approach. We analyze and compare the time and memory resources required by all these solutions, which are suited to different applications, and we show that it is possible to get accurate results greatly reducing memory demand compared with the standard solution at almost the same speed

Memory and computation effective approaches for i–vector extraction

This paper focuses on the extraction of i-vectors, a compact representation of spoken utterances that is used by most of the state–of–the–art speaker recognition systems. This work was mainly motivated by the need of reducing the memory demand of the huge data structures that are usually precomputed for fast computation of the i-vectors. We propose a set of new approaches allowing accurate i-vector extraction but requiring less memory, showing their relations with the standard computation method introduced for eigenvoices. We analyze the time and memory resources required by these solutions, which are suited to different fields of application, and we show that it is possible to get accurate results with solutions that reduce both computation time and memory demand compared with the standard solution

Fast and Memory Effective I-Vector Extraction Using a Factorized Sub-Space

Most of the state-of-the-art speaker recognition systems use a compact representation of spoken utterances referred to as i-vectors. Since the "standard" i-vector extraction procedure requires large memory structures and is relatively slow, new approaches have recently been proposed that are able to obtain either accurate solutions at the expense of an increase of the computational load, or fast approximate solutions, which are traded for lower memory costs. We propose a new approach particularly useful for applications that need to minimize their memory requirements. Our solution not only dramatically reduces the storage needs for i-vector extraction, but is also fast. Tested on the female part of the tel-tel extended NIST 2010 evaluation trials, our approach substantially improves the performance with respect to the fastest but inaccurate eigen-decomposition approach, using much less memory than any other known method

Factorized Sub-Space Estimation for Fast and Memory Effective I-vector Extraction

Most of the state-of-the-art speaker recognition systems use a compact representation of spoken utterances referred to as i-vector. Since the "standard" i-vector extraction procedure requires large memory structures and is relatively slow, new approaches have recently been proposed that are able to obtain either accurate solutions at the expense of an increase of the computational load, or fast approximate solutions, which are traded for lower memory costs. We propose a new approach particularly useful for applications that need to minimize their memory requirements. Our solution not only dramatically reduces the memory needs for i-vector extraction, but is also fast and accurate compared to recently proposed approaches. Tested on the female part of the tel-tel extended NIST 2010 evaluation trials, our approach substantially improves the performance with respect to the fastest but inaccurate eigen-decomposition approach, using much less memory than other method

Fast and Memory Effective I-Vector Extraction Using a Factorized Sub-Space

Most of the state–of–the–art speaker recognition systems use a compact representation of spoken utterances referred to as i–vectors. Since the ”standard” i–vector extraction procedure requires large memory structures and is relatively slow, new approaches have recently been proposed that are able to obtain either accurate solutions at the expense of an increase of the computational load, or fast approximate solutions, which are traded for lower memory costs. We propose a new approach particularly useful for applications that need to minimize their memory requirements. Our solution not only dramatically reduces the storage needs for i–vector extraction, but is also fast. Tested on the female part of the tel-tel extended NIST 2010 evaluation trials, our approach substantially improves the performance with respect to the fastest but inaccurate eigen-decomposition approach, using much less memory than any other known method

A Hybrid Approach with Multi-channel I-Vectors and Convolutional Neural Networks for Acoustic Scene Classification

In Acoustic Scene Classification (ASC) two major approaches have been followed . While one utilizes engineered features such as mel-frequency-cepstral-coefficients (MFCCs), the other uses learned features that are the outcome of an optimization algorithm. I-vectors are the result of a modeling technique that usually takes engineered features as input. It has been shown that standard MFCCs extracted from monaural audio signals lead to i-vectors that exhibit poor performance, especially on indoor acoustic scenes. At the same time, Convolutional Neural Networks (CNNs) are well known for their ability to learn features by optimizing their filters. They have been applied on ASC and have shown promising results. In this paper, we first propose a novel multi-channel i-vector extraction and scoring scheme for ASC, improving their performance on indoor and outdoor scenes. Second, we propose a CNN architecture that achieves promising ASC results. Further, we show that i-vectors and CNNs capture complementary information from acoustic scenes. Finally, we propose a hybrid system for ASC using multi-channel i-vectors and CNNs by utilizing a score fusion technique. Using our method, we participated in the ASC task of the DCASE-2016 challenge. Our hybrid approach achieved 1 st rank among 49 submissions, substantially improving the previous state of the art