Endpoint detection in a stand-alone real-time voice recognition system

A method of recognizing voice in real time by converting a sampled voice signal, having voice portions and noise portions, in digital form to a reference pattern in a training mode and outputting recognition results in recognition mode, comprising the steps of:

• preprocessing by prefiltering said sampled voice signal through a first order filter to emphasize the high frequency components of the sampled voice signal in digital form and to obtain a prefiltered signal:
• feature extraction by framing said prefiltered signal to produce a framed signal, filtering said frame signal by a Hamming window function and by a Durbin algorithm to result in multi-order fixed point linear prediction coefficients;
• voice end-point detection by computing said voice portions and eliminating said noise portions using the following steps:
• step 1:
• define a length L of time of said voice to be zero,
• step 2:
• fetch one frame to compute the energy coefficients E, where ##EQU4## S(i) is the amplitude of said sampled voice signal, step 3:
• test whether E>=a predetermined noise threshold, if "no", go to step 2,
• step 4:
• set Flag=0 where Flag is a Boolean variable to indicate that the sampled voice signal is a single tone,
• step 5:
• set a width D, the length of a single tone of said voice, D=0,
• step 6:
• increase D by 1 and fetch next frame to compute the energy coefficient E, and if E>=the predetermined noise threshold, stay at step 6 until E<the predetermined noise threshold,
• step 7:
• let L=L+D,
• step 8:
• if Flag=0, as set in step 4 and D<8, go to step 1,
• if Flag=0 and D>=8, then BTW=0, where BTW is a distance between one said single tone and another said single tone, Flag=1, go to step 9,
• if Flag=1 and D<8, then BTW=BTW+D, go to step 9,
• if Flag=1 and D>=8, then BTW=0, go to step 9,
• step 9:
• if E<the predetermined noise threshold and BTW<16, then BTW=BTW+1, and fetch next frame to compute E, and go to step 9,
• step 10:
• if BTW<16, set L=L+BTW and go to step 5,
• step 11:
• set L=L-BTW, clear BTW and output L,
• step 12:
• end said end-point detection;
• in said training mode, storing said multi-order fixed point linear prediction coefficients as a reference pattern in a memory, and going back to said preprocessing step;
• in said recognition mode, storing said multi-order coefficients by a dynamic time warping method in a modified symmetric form, comparing updated said coefficients with said reference pattern obtained previously during said training mode, and outputting result;
• said modified symmetric form using a one-dimensional circular buffer with only 2*n+1 space in said memory instead of n*n space for a 2-dimensional memory, where n is the adjustable size of said dynamic time warping window and is adjustable.