Noise Reduction in Speech Applications

Gillian M. Davis

• Publisher: CRC Press
• Year: 2018
• Print ISBN: 9780849309496
• eBook ISBN: 9781351835992
• Topic: Technology & Engineering
• Subtopic: Electrical Engineering & Telecommunications
• Index: Technology & Engineering

Noise and distortion that degrade the quality of speech signals can come from any number of sources. The technology and techniques for dealing with noise are almost as numerous, but it is only recently, with the development of inexpensive digital signal processing hardware, that the implementation of the technology has become practical. Noise Reduction in Speech Applications provides a comprehensive introduction to modern techniques for removing or reducing background noise from a range of speech-related applications. Self-contained, it starts with a tutorial-style chapter of background material, then focuses on system aspects, digital algorithms, and implementation. The final section explores a variety of applications and demonstrates to potential users of the technology the results possible with the noise reduction techniques presented. The book offers chapters contributed by international experts, a practical, systems approach, and numerous references. For electrical, acoustics, signal processing, communications, and bioengineers, Noise Reduction in Speech Applications is a valuable resource that shows you how to decide whether noise reduction will solve problems in your own systems and how to make the best use of the technologies available.

Section IV:

Special Applications

9

Signal and Feature Compensation Methods for Robust Speech Recognition

Rita Singh, Richard M. Stern and Bhiksha Raj

CONTENTS

Introduction

Speech Recognition as Statistical Pattern Classification

Effect of Noise on Speech Recognition Systems

Compensating for the Effects of Noise

Signal Compensation

Linear Spectral Subtraction

Nonlinear Spectral Subtraction

Wiener Filtering

Feature Compensation

Multivariate Gaussian-Based Cepstral Normalization

Vector Taylor Series Compensation

Codeword-Dependent Cepstral Normalization

Cepstral and Spectral High-Pass Filtering

Discussion of Relative Merits of the Methods

Acknowledgments

References

Introduction

As computing, communication, and other electronic devices become physically smaller and attempt to perform increasingly complicated functions, traditional interfaces such as buttons, keyboards, etc. become difficult to use. Speech is a much more natural and simpler interface for such devices, especially if they are to be remotely operated. Viable technology currently exists for the deployment of speech-enabled devices in controlled environmental conditions. However, as these devices are deployed in increasingly difficult operating conditions that are open to uncontrolled noises and acoustical disturbances, the performance of speech recognition systems degrades greatly. This chapter and Chapter 10 are concerned with the subject of development of techniques that reverse this degradation.

Broadly, techniques that enhance environmental robustness for speech recognition systems can be divided into two categories: techniques that operate on speech signals or the features derived from them prior to the recognition process, and techniques that modify the recognition system to perform optimally on incoming noisy speech signals. In this chapter, we review techniques that modify incoming signals or feature vectors. Techniques that modify the structure or parameters of the speech recognition system are discussed in Chapter 10.

For the benefit of readers with a limited background in speech recognition technologies, we begin by reviewing the formulation of automatic speech recognition as a statistical pattern classification process and by discussing how environmental disturbances adversely affect classifier performance. Later sections describe selected signal and feature compensation techniques in current usage, which were chosen on the basis of their efficiency and generality.

Speech Recognition as Statistical Pattern Classification

Automatic speech recognition systems are pattern classifiers designed to solve a rather specific statistical pattern classification problem. A simple example of a statistical pattern classification problem is that of determining the member of a set of N classes C₁, C₂,…, C_N to which a specific vector X_s belongs, knowing that it does belong to one of the classes.

Let $P(X|C_i)$ be the known distribution of all vectors belonging to class C_i. Let αi be the fraction of all data points that belong to class C_i; αi is also known as the a priori probability of C_i. It can be shown that if the data vector X_s is assigned to a class according to the following rule¹:

$X_s\to C_{i}if{\alpha }_{i}P(X_{s|}{C}_i)>{\alpha }_{j}P(X_s|C_j)f\text{o}\text{r}a\text{l}\text{l}j\ne i$

(9.1)

the expected classification error is minimum. In other words, given an infinitely large set of data points to classify, the total number of misclassified points will be minimum if the classification rule above is followed. Pattern classifiers based on the above rule are known as Bayesian classifiers. If the criterion for classification is other than that of minimum expected classification error, e.g., that of minimizing the expected cost of classification (known as minimum risk classification), where the cost may be any function of the output of the classifier, the actual classification rule can vary from the one given above, but its form will still be very similar.

Speech recognition is the problem of determining the sequence of words that were spoken in an utterance, given the recorded signal for that utterance. We can consider the set of all signals that are instances of a particular word sequence to form the class of signals representing that word sequence. Hence, there is a class of signals associated with every possible sequence of words in a language. Statistical speech recognition can be stated as the problem of determining to which of these classes a given signal belongs. This problem can now be treated as an instance of the Bayesian classification ...

Table of contents

• Cover
• Half Title
• Title Page
• Copyright Page
• Table of Contents
• Section I Tutorial
• Section II System Aspects
• Section III Digital Algorithms and Implementation
• Section IV Special Applications
• Index