Issam Bazzi

A passive approach for detecting shared bottlenecks

There is a growing interest in discovering Internet path characteristics using end-to-end measurements. However, the current mechanisms for performing this task either send probe traffic, or require the sender to cooperate by time stamping the packets or sending them back-to-back. Furthermore, most of these techniques require the packets to carry sequence numbers to detect losses, and a few of them assume the existence of multicast. This paper introduces a completely passive approach for learning Internet path characteristics. In particular, we show that by noting the time difference between consecutive packets, a passive observer can cluster the flows into groups, such that all the flows in one group share the same bottleneck. Our approach relies on the observation that the correct clustering minimizes the entropy of the inter-packet spacing seen by the observer. It does not inject any probe traffic into the network, does not require any cooperation from the senders, and works with any type of traffic whether it is TCP, UDP, or even multicast.

A comparison and combination of methods for OOV word detection and word confidence scoring

This paper examines an approach for combining two different methods for detecting errors in the output of a speech recognizer. The first method attempts to alleviate recognition errors by using an explicit model for detecting the presence of out-of-vocabulary (OOV) words. The second method identifies potentially misrecognized words from a set of confidence features extracted from the recognition process using a confidence scoring model. Since these two methods are inherently different, an approach which combines the techniques can provide significant advantages over either of the individual methods. In experiments in the JUPITER weather domain, we compare and contrast the two approaches and demonstrate the advantage of the combined approach. In comparison to either of the two individual approaches, the combined approach achieves over 25% fewer false acceptances of incorrectly recognized keywords (from 55% to 40%) at a 98% acceptance rate of correctly recognized keywords.

Tracking Vocal Tract Resonances Using a Quantized Nonlinear Function Embedded in a Temporal Constraint

This paper presents a new technique for high-accuracy tracking of vocal-tract resonances (which coincide with formants for nonnasalized vowels) in natural speech. The technique is based on a discretized nonlinear prediction function, which is embedded in a temporal constraint on the quantized input values over adjacent time frames as the prior knowledge for their temporal behavior. The nonlinear prediction is constructed, based on its analytical form derived in detail in this paper, as a parameter-free, discrete mapping function that approximates the “forward” relationship from the resonance frequencies and bandwidths to the Linear Predictive Coding (LPC) cepstra of real speech. Discretization of the function permits the “inversion” of the function via a search operation. We further introduce the nonlinear-prediction residual, characterized by a multivariate Gaussian vector with trainable mean vectors and covariance matrices, to account for the errors due to the functional approximation. We develop and describe an expectation–maximization (EM)-based algorithm for training the parameters of the residual, and a dynamic programming-based algorithm for resonance tracking. Details of the algorithm implementation for computation speedup are provided. Experimental results are presented which demonstrate the effectiveness of our new paradigm for tracking vocal-tract resonances. In particular, we show the effectiveness of training the prediction-residual parameters in obtaining high-accuracy resonance estimates, especially during consonantal closure.

Heterogeneous lexical units for automatic speech recognition: preliminary investigations

This paper explores the use of the phone and syllable as primary units of representation in the first stage of a two-stage recognizer. A finite-state transducer speech recognizer is utilized to configure the recognition as a two-stage process, where either phone or syllable graphs are computed in the first stage, and passed to the second stage to determine the most likely word hypotheses. Preliminary experiments in a weather information speech understanding domain show that a syllable representation with either bigram or trigram language models provides more constraint than a phonetic representation with a higher-order n-gram language model (up to a 6-gram), and approaches the performance of a more conventional single-stage word-based configuration.

An expectation maximization approach for formant tracking using a parameter-free non-linear predictor

This paper presents a new approach for formant tracking using a parameter-free non-linear predictor that maps formant frequencies and bandwidths into the acoustic feature space. The approach relies on decomposing the speech signal into two components: the first component captures the mapping between formants and acoustic observations, while the second component is intended to capture the residual in the signal. We build the mapping by quantizing the formant space and creating a predictor codebook. Formant tracking is achieved by: (1) EM training of the parameters of the residual component, and (2) searching the predictor codebook for the best formant values. We explore both MAP and MMSE methods for performing formant tracking with the proposed approach. Furthermore, we impose first order continuity constraints on formant trajectories, and use Viterbi search to perform formant tracking. We present formant tracking results on data from the Switchboard corpus.