We describe a speaker tracking and detection system, for Switchboard conversations, that uses a two-speaker and silence hidden Markov model (HMM) with a minimum state duration constraint and Gaussian mixture model (GMM) state distributions adapted from a single genderand handset-independent imposter model distribution. Speaker tracking is used to segment speakers for detection, which is carried...

We consider a city where induction-based vehicle count sensors are installed at some, but not all street junctions. Each sensor regularly outputs a count and a saturation value. We first use a discrete time Gauss-Markov model based on historical data to predict the evolution of these saturation values, and then a Gaussian Process derived from the street graph to extend these predictions to all ...

Este artículo describe el conjunto de experimentos realizados para obtener el reconocimiento de 60 notas musicales de un piano digital por medio de técnicas de procesamiento digital de señales y clasificadores. Para la etapa de técnicas de procesamiento digital de señales se utilizaron: Frecuencia fundamental, coeficientes Cepstrales en la Frecuencia de Mel y Cepstrales de Mecánica Coclear. Par...

In this paper we introduce a novel hybrid model architecture for speech recognition and investigate its noise robustness on the Aurora 2 database. Our model is composed of a bidirectional Long Short-Term Memory (BLSTM) recurrent neural net exploiting long-range context information for phoneme prediction and a Dynamic Bayesian Network (DBN) for decoding. The DBN is able to learn pronunciation va...

A semi-continuous segmental probability model, which can be considered as a special form of continuous mixture segmental probability model with continuous output probability density functions sharing in a mixture Gaussian density codebook, is proposed in this paper. The amount of training data required, as well as the computational complexity of the semi-continuous segmental probability model(S...

An algorithm is proposed that achieves a good trade-oo between modeling resolution and robustness by using a new, general scheme for tying of mixture components in continuous mixture-density hidden Markov model (HMM)-based speech recognizers. The sets of HMM states that share the same mixture components are determined automatically using agglomerative clustering techniques. Experimental results...

The analysis of routinely collected surveillance data is an important challenge in public health practice. We present a method based on a hidden Markov model for monitoring such time series. The model characterizes the sequence of measurements by assuming that its probability density function depends on the state of an underlying Markov chain. The parameter vector includes distribution paramete...

There has been significant interest in developing new forms of acoustic model, in particular models which allow additional dependencies to be represented than allowed within a standard hidden Markov model (HMM). This paper discusses one such class of models, augmented statistical models. Here a locally exponential approximation is made about some point on a base distribution. This allows additi...

In a real environment, it is essential to adapt acoustic models to variations in background noises in order to realize robust speech recognition. In this paper, we construct an extended acoustic model by combining a mismatch model with a clean acoustic model trained using only clean speech data. We assume the mismatch model conforms to a Gaussian distribution with timevarying population paramet...