Voiced/nonvoiced detection in compressively sensed speech signals

2016 24th European Signal Processing Conference (EUSIPCO)

Sao

2016

In compressed sensing (CS) framework, a signal is sampled below Nyquist rate, and the acquired compressed samples are generally random in nature. However, for efficient estimation of the actual signal, the sensing matrix must preserve the relative distances among the acquired compressed samples. Provided this condition is fulfilled, we show that CS samples will preserve the envelope of the actual signal even at different compression ratios. Exploiting this envelope preserving property of CS samples, we propose a new fast dictionary learning (DL) algorithm which is able to extract prototype signals from compressive samples for efficient sparse representation and recovery of signals. These prototype signals are orthogonal intrinsic mode functions (IMFs) extracted using empirical mode decomposition (EMD), which is one of the popular methods to capture the envelope of a signal. The extracted IMFs are used to build the dictionary without even comprehending the original signal or the sensing matrix. Moreover, one can build the dictionary on-line as new CS samples are available. In particularly, to recover first L signals (∈ R n ) at the decoder, one can build the dictionary in just O(nL log n) operations, that is far less as compared to existing approaches. The efficiency of the proposed approach is demonstrated experimentally for recovery of speech signals.

Section: Discussionmentioning

confidence: 99%

Making sense of randomness: Fast signal recovery from compressive samples

2016 24th European Signal Processing Conference (EUSIPCO)

Sao

2016

“…This property can be exploited for estimating efficient representations for a speech signal and sparse coding is one of the methods to estimate such representations [13]. In recent years sparse coding based signal processing has been applied to various speech processing applications such as speech recognition [1], speech enhancement [14], speech coding [15] and voiced/nonvoiced detection [16].…”

Section: Sparse Coding For Speech Signalsmentioning

confidence: 99%

Class specific GMM based sparse feature for speech units classification

2017 25th European Signal Processing Conference (EUSIPCO)

Dileep

et al. 2017

Self Cite

Abstract-In this paper, features based on the sparse representation (SR) are proposed for the classification of speech units. The proposed method employs multiple dictionaries to effectively model variations present in the speech signal. Here, a Gaussian mixture model (GMM) is built using spectral features corresponding to frames of all the examples of a speech class. Multiple dictionaries corresponding to different mixture are learned using the respective speech frames. Given a train/test speech frame, minimum spectral distance measure from the GMM means is employed to select an appropriate dictionary. The selected dictionary is used to obtain the sparse feature representation, which is used for the classification of speech units. The effectiveness of the proposed feature is demonstrated using continuous density hidden Markov model (CDHMM) based classifiers for (i) classification of isolated utterances of E-set of English alphabet, (ii) classification of consonant-vowel (CV) segments in Hindi language and (iii) classification of phoneme from TIMIT phonetic corpus. Experimental results reveal that the proposed features outperforms existing feature representations for various speech units classification tasks.

“…CS/SR have recently drawn much interest in the field of speech processing [5][6][7]. According to the theory of CS, a signal can be reconstructed with minimum error from less number of measurements, provided that signal has a sparse representation in some domain/dictionary [8].…”

Section: Compressed Sensing For Speech Signalsmentioning

confidence: 99%

Compressed sensing for unit selection based speech synthesis

2015 23rd European Signal Processing Conference (EUSIPCO)

Sao

2015

Self Cite

This paper proposes an approach based on compressed sensing to reduce the footprint of speech corpus in unit selection based speech synthesis (USS) systems. It exploits the observation that speech signal can have a sparse representation (in suitable choice of basis functions) and can be estimated effectively using the sparse coding framework. Thus, only few significant coefficients of the sparse vector needed to be stored instead of entire speech signal. During synthesis, speech signal can be reconstructed (with less error) using these significant coefficients only. Furthermore, the number of significant coefficients can be chosen adaptively based on type of segment such as voiced or unvoiced. Simulation results suggest that the proposed compression method effectively preserves most of the spectral information and can be used as an alternative to existing compression methods used in USS systems.