1. Spoken Digit Recognition
Yi-Pei Chen
5/2/2016

2. Motivation

3. Steps Speech Acquisition Signal Preprocessing Feature Extraction
Continuous speech waveform
Elimination of background noise
Framing
Windowing
MFCC
Classifier
Output
Preprocessing is elimination of back ground noise, framing and windowing.
Back ground noise is removed from the data.
Continuous speech has been separated into frames. That method is known as framing.
Windowing is used to determine the portion of the speech signal.
Feature Extraction
identify the components of the audio signal that are good for identifying the linguistic content
MLP
KNN
SVM

4. Mel Frequency Cepstral Coefficients
Speech:
sounds generated by a human are filtered by the shape of the vocal tract including tongue, teeth etc.
Process:
Take the Fourier transform of (a windowed excerpt of) a signal.
Map the powers of the spectrum onto the mel scale, using triangular overlapping windows.
Take the logs of the powers at each of the mel frequencies.
Take the discrete cosine transform of the list of mel log powers, as if it were a signal.
The MFCCs are the amplitudes of the resulting spectrum.
This shape determines what sound comes out.
The shape of the vocal tract manifests itself in the envelope of the short time power spectrum, and the job of MFCCs is to accurately represent this envelope.
If we can determine the shape accurately, this should give us an accurate representation of the phoneme being produced.

5. Dataset: Spoken Arabic Digit
44 males and 44 females’ native Arabic speakers
collected by the Laboratory of Automatic and Signals, University of Badji- Mokhtar Annaba, Algeria
8800(10 digits x 10 repetitions x 88 speakers) time series of 13 MFCCs
Sampling rate: 11025 Hz, 16 bits
Window applied: hamming window

6. Current State
Working on choosing the best classifier

7. References
M.Kalamani, Dr.S.Valarmathy, S.Anitha. “Automatic Speech Recognition using ELM and KNN Classifiers” Vol 3, Issue 4, April 2015, IJIRCCE
RICHARD P. LIPPMANN. “Neural Network Classifiers for Speech Recognition” The Lincoln Laboratory Journal, Volume 1, Number 1 (1988), 1-18, MIT
Jean Hennebert, Martin Hasler and Hervé Dedieu , “Neural Networks in Speech Recognition” Department of Electrical Engineering, Swiss Federal Institute of Technology
Abdul Ahad, Ahsan Fayyaz, Tariq Mehmood. “Speech Recognition using Multilayer Perceptron” p , Vol.1, Students Conference, ISCON '02. Proceedings. IEEE
Issam Bazzi. “Using Support Vector Machines for Spoken Digit Recognition” p.48-49, MIT Laboratory for Computer Science Spoken Language Systems Group
MFCC tutorial: frequency-cepstral-coefficients-mfccs/