Publication

Continuous Speech Recognition using Boosted Binary Features

Mathew Magimai Doss
2011
Reports

Abstract

A novel parts-based binary-valued feature termed Boosted Binary Feature (BBF) was recently proposed for ASR. Such features look at specific pairs of time-frequency bins in the spectro-temporal plane. The most discriminative of these features are selected by boosting and integrated into a standard HMM-based system using multilayer perceptron (MLP) and single layer perceptron (SLP). Previous studies on TIMIT phoneme recognition task showed that BBF yields similar or better performance compared to cepstral features. In this work, this study is extended to continuous speech recognition task on the DARPA Resource Management database. Results show that BBF achieves comparable word error rate (5.5%) on this task with respect to standard cepstral features (5.1%) using MLP. Using SLP, the error rate for BBF shows much lower degradation (from 5.5% to 7.1%) compared to cepstral features (from 5.1% to 14.7%). In addition, it is found that BBF features can be selected well using auxiliary data.

Official source

https://infoscience.epfl.ch/entities/publication/376565ab-f81e-4965-9e97-9426cf167345

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Mathew Magimai Doss
2011
Reports

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/376565ab-f81e-4965-9e97-9426cf167345

About this result

Related concepts (24)

Speech recognition

Speech recognition is an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers. It is also known as automatic speech recognition (ASR), computer speech recognition or speech to text (STT). It incorporates knowledge and research in the computer science, linguistics and computer engineering fields. The reverse process is speech synthesis.

Multilayer perceptron

A multilayer perceptron (MLP) is a misnomer for a modern feedforward artificial neural network, consisting of fully connected neurons with a nonlinear kind of activation function, organized in at least three layers, notable for being able to distinguish data that is not linearly separable. It is a misnomer because the original perceptron used a Heaviside step function, instead of a nonlinear kind of activation function (used by modern networks).

Perceptron

In machine learning, the perceptron (or McCulloch-Pitts neuron) is an algorithm for supervised learning of binary classifiers. A binary classifier is a function which can decide whether or not an input, represented by a vector of numbers, belongs to some specific class. It is a type of linear classifier, i.e. a classification algorithm that makes its predictions based on a linear predictor function combining a set of weights with the feature vector.

Related publications (12)

Continuous Speech Recognition using Boosted Binary Features

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