Speaker independent continuous speech recognition

Speaker independent continuous speech recognition

This report presents a detail study on psychoacoustic modeling for feature extraction for robust continuous speech recognition. In an automatic speech recognition (ASR) system, feature extraction is critical to determining the recognizer’s performance. The most popular feature vectors for ASR are Me...

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Bibliographic Details
Main Author: Soon, Ing Yann.
Other Authors: School of Electrical and Electronic Engineering
Format: Research Report
Language:English
Published: 2012
Subjects:
DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
Online Access:http://hdl.handle.net/10356/47728
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Institution: Nanyang Technological University
Language: English
Description
Summary:This report presents a detail study on psychoacoustic modeling for feature extraction for robust continuous speech recognition. In an automatic speech recognition (ASR) system, feature extraction is critical to determining the recognizer’s performance. The most popular feature vectors for ASR are Mel Frequency Cepstral Coefficients (MFCC). However, it is also well known that its performance drops dramatically under noisy condition. One of the objectives of this research is to improve on the robustness of a continuous speech recognizer.

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