a study on detection based automatic speech recognition
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A Study on Detection Based Automatic Speech Recognition. Author : Chengyuan Ma Yu Tsao Professor: 陳嘉平 Reporter : 許峰閤. Outline. Introduction Word detector design Hypotheses combination Experiment. Introduction.

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a study on detection based automatic speech recognition

A Study on Detection Based Automatic Speech Recognition

Author : Chengyuan Ma

Yu Tsao

Professor:陳嘉平

Reporter :許峰閤

outline
Outline
  • Introduction
  • Word detector design
  • Hypotheses combination
  • Experiment
introduction
Introduction
  • The current ASR system is top-down and this is a bottom-up system.
  • It include:

1.word detector.

2.word hypothesis verification and false

alarm pruning.

3.Hypothesis combination.

word detector design
Word detector design
  • We have separate detector for each lexical item in the vocabulary.
  • HMM model are used for detector design.
  • The key issue is how to choose an appropriate grammer network.
word verification and pruning7
Word verification and pruning
  • It’s obvious that these detectors generate a lot of false alarms.
  • Here are three pruning strategies will be presented.
word verification and pruning8
Word verification and pruning
  • Temporal information based pruning:

For example, the duration of the word “one” should be greater than 150 ms.

  • Attributes model based pruning:

Each word has its own attribute sequence pattern.

  • Signal based pruning:

Signal feature based pruning.

For example, we know the energy of a nasalsound is often concentrated on the low frequency region.

hypotheses combination
Hypotheses combination
  • We investigate hypothesis combination strategies using outputs from all detectors to generate a word string.
  • The weighted directed graph is one of the methods that can be used to combine the detector output into a digit string.
hypotheses combination11
Hypotheses combination
  • Each node in the graph is a detected digit boundary.
  • The number in the node is the time stamp.
  • The number beside each edge is the frame average log-likelihood.
  • We can use the Dijkstra’s algorithm to find the shortest path.
experiment
Experiment
  • Conduct on the TIDIGITS corpus.
  • Digit vocabulary is made of 11 digits, one to nine, plus oh and zero.
  • 12-dimensional MFCC is used for frond-end processing.
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