Publications

In this paper we propose a multiresolution short-time analysis method for speech enhancement. It is well known that fixed resolution methods such as the traditional short-time Fourier transform do not generally match the time-frequency structure of the signal being analyzed resulting in poor estimates of the speech and noise spectra...

Discriminative training for language recognition has been a key tool for improving system performance. In addition, recognition directly from shifted-delta cepstral features has proven effective. A recent successful example of this paradigm is SVM-based discrimination of languages based on GMM mean supervectors (GSVs). GSVs are created through MAP adaptation of...

Speaker recognition using support vector machines (SVMs) with features derived from generative models has been shown to perform well. Typically, a universal background model (UBM) is adapted to each utterance yielding a set of features that are used in an SVM. We consider the case where the UBM is a...

This paper considers the problem of obtaining an accurate spectral representation of speech formant structure when the voicing source exhibits a high fundamental frequency. Our work is inspired by auditory perception and physiological modeling studies implicating the use of temporal changes in speech by humans. Specifically, we develop and assess...

One commonly used approach for language recognition is to convert the input speech into a sequence of tokens such as words or phones and then to use these token sequences to determine the target language. The language classification is typically performed by extracting N-gram statistics from the token sequences and...

We present two approaches to noise robust very low bit rate speech coding using wideband MELP analysis/synthesis. Both methods exploit multiple acoustic and non-acoustic input sensors, using our previously-presented dynamic waveform fusion algorithm to simultaneously perform waveform fusion, noise suppression, and crosschannel noise cancellation. One coder uses a 600 bps...

In this paper we describe the application of a feature-space transform based on constrained maximum likelihood linear regression for unsupervised compensation of channel and speaker variability to the language recognition problem. We show that use of such transforms can improve baseline GMM-based language recognition performance on the 2005 NIST Language...

Regions of nonmodal phonation, which exhibit deviations from uniform glottal-pulse periods and amplitudes, occur often in speech and convey information about linguistic content, speaker identity, and vocal health. Some aspects of these deviations are random, including small perturbations, known as jitter and shimmer, as well as more significant aperiodicities. Other...

In this paper, we investigate the problem of topic identification from audio documents using features extracted from speech recognition lattices. We are particularly interested in the difficult case where the training material is minimally annotated with only topic labels. Under this scenario, the lexical knowledge that is useful for topic...

There have been numerous recent strides at making sinewave analysis consistent with time-varying sinewave models. This is particularly important in high-frequency speech regions where harmonic frequency modulation (FM) can be significant. One notable approach is through the Fan Chirp transform that provides a set of FM-sinewave basis functions consistent with...