Speech recognition is the classical problem in pattern recognition research field. However, just a few graph based machine learning methods have been applied to this classical problem. In this paper, we propose the un-normalized graph p-Laplacian semi-supervised learning methods and these methods will be applied to the speech network constructed from the MFCC speech dataset to predict the label...

    Background: Intelligibility refers to understandability of speech; and lack of it can negatively affect children’s overall communication effectiveness. Children with repaired cleft lip and/or cleft palate (CL/P) may experience poor speech intelligibility. This study aimed at evaluating speech intelligibility in children with repaired CL/P who had not been referred to sp...

The objective intelligibility assessment of nonlinearly enhanced speech is a widely experienced problem. Nonlinear speech enhancement processors operate primarily on the low-level and transient components of speech. As these sections contain important acoustic cues as well as context-constitutive information, they dominate speech intelligibility. For that reason, shorttime intelligibility measu...

The field of computational auditory scene analysis (CASA) strives to build computer models of the human ability to interpret sound mixtures as the combination of distinct sources. A major obstacle to this enterprise is defining and incorporating the kind of high level knowledge of real-world signal structure exploited by listeners. Speech recognition, while typically ignoring the problem of non...

We present an algorithm to perform blind, one-microphone speech separation. Our algorithm separates mixtures of speech without modeling individual speakers. Instead, we formulate the problem of speech separation as a problem in segmenting the spectrogram of the signal into two or more disjoint sets. We build feature sets for our segmenter using classical cues from speech psychophysics. We then ...

In this paper an estimator for speech enhancement based on Laplacian Mixture Model has been proposed. The proposed method, estimates the complex DFT coefficients of clean speech from noisy speech using the MMSE  estimator, when the clean speech DFT coefficients are supposed mixture of Laplacians and the DFT coefficients of  noise are assumed zero-mean Gaussian distribution. Furthermore, the MMS...

In this paper we address the problem of aligning very long (often more than one hour) audio files to their corresponding textual transcripts in an effective manner. We present an efficient recursive technique to solve this problem that works well even on noisy speech signals. The key idea of this algorithm is to turn the forced alignment problem into a recursive speech recognition problem with ...

Speech compression is a key technology underlying digital cellular communications, VoIP, voicemail, and voice response systems. We trace the evolution of speech coding based on the linear prediction model, highlight the key milestones in speech coding, and outline the structures of the most important speech coding standards. Current challenges, future research directions, fundamental limits on ...