Automated nasal feature detection for the lexical access from features project

The focus of this thesis was the design, implementation, and evaluation of a set of automated algorithms to detect nasal consonants from the speech waveform in a distinctive feature-based speech recognition system. The study used a VCV database of over 450 utterances recorded from three speakers, two male and one female. The first stage of processing for each speech waveform included automated 'pivot' estimation using the Consonant Landmark Detector - these 'pivots' were considered possible sonorant closures and releases in further analyses. Estimated pivots were analyzed acoustically for the nasal murmur and vowel-nasal boundary characteristics. For nasal murmur, the analyzed cues included observing the presence of a low frequency resonance in the short-time spectra, stability in the signal energy, and characteristic spectral tilt. The acoustic cues for the nasal boundary measured the change in the energy of the first harmonic and the net energy change of the 0-350Hz and 350-1000Hz frequency bands around the pivot time. The results of the acoustic analyses were translated into a simple set of general acoustic criteria that detected 98% of true nasal pivots. The high detection rate was partially offset by a relatively large number of false positives - 16% of all non-nasal pivots were also detected as showing characteristics of the nasal murmur and nasal boundary. The advantage of the presented algorithms is in their consistency and accuracy across users and contexts, and unlimited applicability to spontaneous speech.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2004

Author dc:creator

• Hajro, Neira, 1978-

Advisor dc:contributor.advisor

• Kenneth N. Stevens.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

en_US

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/28401

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/28401