Method and apparatus for discriminating speech from voice-band data in a communication network
View Patent ↗A method and an apparatus accurately discriminates between speech and voice-band data (VBD) in a communication network by calculating self similarity ratio (SSR) values, which indicate periodicity characteristics of an input signal segment, and/or autocorrelation coefficients, which indicate spectral characteristics of an input signal segment, to generate a speech/VBD discrimination result. In one implementation, the speech-VBD discriminating apparatus calculates both short-term delay and long-term delay SSR values to analyze the repetition rate of an input signal frame, thereby indicating whether the input signal frame has the periodicity characteristics of a typical speech signal or a VBD signal. The speech-VBD discriminating apparatus further calculates a plurality of short-term autocorrelation coefficients to determine the spectral envelope of an input frame, thereby facilitating accurate speech/VBD discrimination. According to one implementation of the present invention, the speech-VBD discriminating apparatus relies on sequential decision logic which improves classification performance by recognizing that changes from speech to VBD or vice versa in a communication medium are unlikely, and discounts discrimination results for relatively low-power signal portions which are more susceptible to errors to further improve discrimination accuracy.
1. A method of discriminating speech from voice-band data in a communication network, comprising:
calculating a self similarity ratio value, representing a periodicity characteristic, and an autocorrelation coefficient value, representing a spectral characteristic, for an input signal segment, wherein calculating the self similarity ratio value includes calculating a plurality of different self similarity ratio values and selecting the highest one of the plurality of different self similarity ratio values as the calculated self similarity ratio value; and
determining whether said input signal segment is speech or voice-band data based on said at least one of said self similarity value and said autocorrelation coefficient value.
2. The invention as defined in claim 1 , wherein said input signal segment is a frame of N samples.
3. The method of claim 1 , wherein said self similarity ratio is calculated based on more than one sample.
4. The invention as defined in claim 1 , wherein
said calculating step calculates a first self similarity ratio value, corresponding to a first sample delay, as a first periodicity characteristic value; and
said determining step determines that said input signal segment is voice-band data if said first self similarity ratio value is greater than a first similarity threshold.
5. The invention as defined in claim 4 , wherein
said calculating step calculates a second self similarity ratio value, corresponding to a second sample delay, as a second periodicity characteristic value, said second sample delay being greater than said first sample delay; and
said determining step determines that said input signal segment is speech if said second self similarity ratio value is greater than a second similarity threshold.
6. The invention as defined in 1 , wherein
said calculating step calculates a first autocorrelation coefficient as a first spectral characteristic value; and
said determining step determines that said input signal segment is voice-band data if said first autocorrelation coefficient is less than a first autocorrelation threshold, and that said input signal segment is speech if said first autocorrelation coefficient is greater than a second autocorrelation threshold, said second autocorrelation threshold being greater than said first autocorrelation threshold.
7. The invention as defined in claim 6 , wherein
said calculating step calculates second and third autocorrelation coefficients as second and third spectral characteristic values respectively, and
said determining step determines that said input signal segment is voice-band data if said second autocorrelation coefficient is less than a third autocorrelation threshold or said third autocorrelation coefficient is less than a fourth autocorrelation threshold.
8. The invention as defined in claim 7 , wherein
said determining step determines that said input signal segment is voice-band data if a sum of said second autocorrelation coefficient and said third autocorrelation coefficient is less than a fifth autocorrelation threshold.
9. The invention as defined in claim 1 , wherein
said calculating and determining steps are performed for a plurality of input signal segments in accordance with a sequential decision logic sequence which designates input signal segments as speech during a speech state and designates input signal segments as voice-band data during a voice-band data state.
10. The invention as defined in claim 9 , wherein
said sequential decision logic sequence switches from said speech state to said voice-band data state when results of said determining step for a plurality of input signal segments indicate that said speech state is erroneous, and
said sequential decision logic sequence switches from said voice-band data state to said speech state when results of said determining step for a plurality of input signal segments indicate that said voice-band data state is erroneous.
11. The invention as defined in claim 9 , wherein
results of said determining step are weighted based on energy content of the corresponding input signal segment so that determination results for low energy input signal segments are given relatively low weight when determining whether to switch from said speech state to said voice-band data state or from said voice-band data state to said speech state.
12. An apparatus for discriminating speech from voice-band data in a communication network, comprising:
calculating means for calculating a self similarity ratio value, representing a periodicity characteristic, and an autocorrelation coefficient value, representing a spectral characteristic, for an input signal segment, wherein calculating the self similarity ratio value includes calculating a plurality of different self similarity ratio values and selecting the highest one of the plurality of different self similarity ratio values as the calculated self similarity ratio value; and
determining means for determining whether said input signal segment is speech or voice-band data based on said at least one of said self similarity value and said autocorrelation coefficient value.
13. The invention as defined in claim 12 , wherein said input signal segment is a frame of N samples.
14. The invention as defined in claim 12 , wherein
said calculating means calculates a first self similarity ratio value, corresponding to a first sample delay, as a first periodicity characteristic value; and
said determining means determines that said input signal segment is voice-band data if said first self similarity ratio value is greater than a first similarity threshold.
15. The invention as defined in claim 14 , wherein
said calculating means calculates a second self similarity ratio value, corresponding to a second sample delay, as a second periodicity characteristic value, said second sample delay being greater than said first sample delay; and
said determining means determines that said input signal segment is speech if said second self similarity ratio value is greater than a second similarity threshold.
16. The invention as defined in 12 , wherein
said calculating means calculates a first autocorrelation coefficient as a first spectral characteristic value; and
said determining means determines that said input signal segment is voice-band data if said first autocorrelation coefficient is less than a first autocorrelation threshold, and that said input signal segment is speech if said first autocorrelation coefficient is greater than a second autocorrelation threshold, said second autocorrelation threshold being greater than said first autocorrelation threshold.
17. The invention as defined in claim 16 , wherein
said calculating means calculates second and third autocorrelation coefficients as second and third spectral characteristic values respectively, and
said determining means determines that said input signal segment is voice-band data if said second autocorrelation coefficient is less than a third autocorrelation threshold or said third autocorrelation coefficient is less than a fourth autocorrelation threshold.
18. The invention as defined in claim 17 , wherein
said determining means determines that said input signal segment is voice-band data if a sum of said second autocorrelation coefficient and said third autocorrelation coefficient is less than a fifth autocorrelation threshold.
19. The invention as defined in claim 12 , wherein
said apparatus classifies a plurality of input signal segments as being either speech or voice-band data in accordance with a sequential decision logic sequence which designates input signal segments as speech during a speech state and designates input signal segments as voice-band data during a voice-band data state.
20. The invention as defined in claim 19 , wherein
said apparatus, in accordance with said sequential decision logic sequence, switches from said speech state to said voice-band data state when results of said determining means for a plurality of input signal segments indicate that said speech state is erroneous, and
said apparatus, in accordance with said sequential decision logic sequence, switches from said voice-band data state to said speech state when results of said determining means for a plurality of input signal segments indicate that said voice-band state is erroneous.
21. The invention as defined in claim 19 , wherein
said apparatus weights results of said determining means based on energy content of the corresponding input signal segment so that determination results for low energy input signal segments are given relatively low weight when said apparatus judges whether to switch from said speech state to said voice-band data state or from said voice-band data state to said speech state.