Method and device for recognizing speech
View Patent ↗A speech is recognized using ACF factors extracted from running autocorrelation functions calculated from the speech. The extracted ACF factors are a W φ(0) (width of ACF amplitude around zero-delay origin), a W φ(0)max (maximum value of the W φ(0) ), a τ 1 (pitch period), a φ 1 (pitch strength), and a Δφ 1 /Δt (rate of the pitch strength change). Syllables in the speech are identified by comparing the ACF factors with templates stored in a database.
1. A method for recognizing speech, comprising steps of:
recording speech signals by a recording unit;
attenuating, by a LPF, a high frequency component of the speech signals received from the recording unit;
converting, by an AD converter, the speech signals received from the LPF from analog signal to digital signal;
storing, by a storage unit, the speech signals received from the AD converter;
reading out, by a processor, the speech signals stored in the storage unit;
calculating, by the processor, running autocorrelation functions from the speech signals read out from the storage unit;
extracting, by the processor, following ACF factors from the running autocorrelation functions:
a W φ(0) which is a width of ACF amplitude around zero-delay origin,
a W φ(0)max which is a maximum value of the W φ(0) ;
a τ 1 which is a pitch period;
a φ 1 which is a pitch strength; and
a Δφ 1 /Δt which is a rate of the pitch strength change;
identifying, by the processor, syllables in the speech signals by comparing the ACF factors with templates stored in a database.
2. The method according to claim 1 , further comprising the step of segmenting, by the processor, the speech signals into the syllables based on the ACF factors.
3. The method according to claim 1 , further comprising the steps of:
extracting ACF factors from the running autocorrelation functions:
a LL which is a listening level calculated by the amplitude at an origin of non-normalized running ACF;
a LL max which is a maximum value of the LL; and
a (τ e ) min which is a minimum value of effective duration τ e ; and
segmenting, by the processor, the speech signals into the syllables based on the LL, the (τ e ) min , the Δφ 1 /Δt, the τ 1 and the W φ(0) .
4. The method according to claim 1 , wherein the identifying step is performed in each of the syllables at time points after a (τ e ) min .
5. The method according to claim 2 , wherein the identifying step is performed in each of the syllables at time points after a (τ e ) min .
6. The method according to claim 3 , wherein the identifying step is performed in each of the syllables at time points after the (τ e ) min .
7. The method according to claim 1 , wherein the identifying step identifies the syllables in the speech signals based on a total distance between the ACF factors and the templates.
8. The method according to claim 2 , wherein the identifying step identifies the syllables in the speech signals based on a total distance between the ACF factors and the templates.
9. The method according to claim 3 , wherein the identifying step identifies the syllables in the speech signals based on a total distance between the ACF factors and the templates.
10. A speech recognition device, comprising:
a recording unit configured to record speech signals;
a LPF configured to attenuate a high frequency component of the speech signals received from the recording unit;
an AD converter configured to convert the speech signals received from the LPF from analog signal to digital signal;
a storage unit configured to store the speech signals received from the AD converter; and
a processor configured to read out the speech signals stored in the storage unit wherein the processor is configured to:
calculate running autocorrelation functions from the speech signals;
extract following ACF factors from the running autocorrelation functions:
a LL which is a listening level calculated by an amplitude at an origin of non-normalized running ACF;
a LL max which is a maximum value of LL;
a W φ(0) which is a width of ACF amplitude around zero-delay origin,
a W φ(0)max which is a maximum value of the W φ(0) ;
a τ 1 which is a pitch period;
a φ 1 which is a pitch strength; and
a Δφ 1 /Δt which is a rate of the pitch strength change; and
identify syllables in the speech signals by comparing the ACF factors with templates stored in a database.
11. The speech recognition device according to claim 10 , wherein the processor is further configured to segment the speech signals into syllables based on the ACF factors.
12. The speech recognition device according to claim 10 ,
wherein the processor further extracts
a LL which is a listening level from the running autocorrelation functions,
a LL max , which is a maximum value of LL, and
a (τ e ) min which is a minimum value of effective duration τ e , and
further configured to segment the speech signals into syllables based on the LL, the (τ e ) min , the Δφ 1 /Δt, the τ 1 and the W φ(0) .
13. The speech recognition device according to claim 10 , wherein the processor identifies each of the syllables at a time point after a (τ e ) min .
14. The speech recognition device according to claim 11 , wherein the processor identifies each of the syllables at a time point after a (τ e ) min .
15. The speech recognition device according to claim 12 , wherein the processor identifies each of the syllables at a time point after the (τ e ) min .
16. The speech recognition device according claim 10 , wherein the processor identifies the syllables in the speech signals based on a total distance between the ACF factors and the templates.
17. The speech recognition device according claim 11 , wherein the processor identifies the syllables in the speech signals based on a total distance between the ACF factors and the templates.
18. The speech recognition device according claim 12 , wherein the processor identifies the syllables in the speech signals based on a total distance between the ACF factors and the templates.