RESPITE: Programme

(RESPITE logo)

ESPRIT Reactive Long Term Research Project:

Recognition of Speech by Partial Information Techniques (RESPITE)

No 28149




  1. Objectives
  2. Workplan
    1. Introduction
    2. Detailed Workplan

1. Objectives

RESPITE will extend and apply two novel technologies &endash; missing data theory and multi-stream theory &endash; to the problem of robust automatic speech recognition (ASR), with particular application to cellular phones and in-car environments. It will also support studies whose purpose is to inform this endeavour.

The specific measurable objectives are to

  1. develop techniques for identifying reliable data;
  2. advance the theory of multi-stream processing;
  3. advance the theory of missing and masked data handling;
  4. inform the above by obtaining new perceptual data on speech recognition.
  5. combine missing data and multistream processing with existing robust ASR methods
  6. evaluate all this within a framework of demonstrator ASR applications to cellular phones and in cars.

1.1 Yardsticks

For the recognition-based objectives (2, 3 and 5) we will use well-established corpus-based evaluation techniques for ASR (for instance word accuracy), which will allow the benefit of each of the above innovations to be quantified in comparison to standard approaches. These studies will be made on standard reference data and on in-house data (see Task 1.1 in section 2.2.2).

For the demonstrators (objective 6), error rates can be measured as the user attempts to accomplish her/his task, under varying conditions.

Yardsticks for identifying reliable data (objective 1) can be based on comparisons between the algorithms' outputs and predefined 'optimal labelling of signal regions, and on recognition results that employ the data deemed to be reliable compared to those based on indiscriminate use of the whole signal.

The success of the perceptual studies (4) can be evaluated indirectly by the extent to which their results are deployed within recognition schemes and the resulting effect on performance. The studies will, in addition, have scientific merit in their own right. In this sense, the measures of success are those of experimental science: has an experiment been designed which will elicit the information required? have results been obtained which are statistically significant? Are these results reproducible? Can the results be understood in terms of the model which provoked the experiment?


2. Workplan

2.1 Introduction

In additional to the person-months accounted for here, substantial additional resources will be committed to the project by its authors and their colleagues.

There are 6 work packages:

In outline, the relation between these is as follows:

2.2 Detailed work plan

For each WP we specify the executing partners and the manager.

WP0 Management

WP1 Resources and Basic Technologies

Task T1.1 Database management

Task T1.2 Baseline recognition systems

WP2 Identifying reliable information

Task T2.1 Computational Auditory Scene Analysis

Task T2.2 Other information-location techniques

WP3 Recognition Techniques

Task T3.1 Developments in noise robust speech recognition

Task T3.2 Missing Data Recognition

Task T3.3 Multi-stream Recognition

Task T3.4 Combining Recognition Techniques

WP4 Application demonstrators and evaluation


Task 4.1 Definition of application demonstrators

Task T4.2 Demonstrator design and evaluation

WP5 Dissemination of Results and Exploitation

Dissemination of results and exploitation is dealt with in detail in section 9. Briefly,

These pages are maintained by Jon Barker,
Last modified: Mon Dec 20 16:21:06 GMT 1999