Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Obstruents are very important acoustical events (i.e., abrupt-consonantal landmarks) in the speech signal.  presents the use of novel Spectral Transition Measure (STM) to locate the obstruents in the continuous speech signal. The problem of obstruent detection involves detection of phonetic boundaries associated with obstruent sounds. ,  propose use of STM information derived from state-of-the-art Mel Frequency Cepstral Coefficients (MFCC) feature set and newly developed feature set, viz., MFCC-TMP (which uses Teager Energy Operator (TEO) to exploit implicitly Magnitude and Phase information in the MFCC framework) for obstruent detection. The key idea here is to exploit capabilities of STM to capture high dynamic transitional characteristics associated with obstruent sounds. The experimental setup is developed on entire TIMIT database. For 20 ms agreement (tolerance) duration, obstruent detection rate is found to be 97.59 % with 17.65 % false acceptance using state-of-the-art MFCC-STM and 96.42 % with 12.88 % false acceptance using MFCC-TMP-STM. Finally, STM-based features along with static representation (i.e., MFCC-STM and MFCC-TMP-STM) are evaluated for phone recognition task.
['Maulik C. Madhavi', 'Hemant A. Patil', 'Bhavik Vachhani']
I am writing a research paper on a idea Spectral transition measure for detection of obstruents
[{"from": "human", "value": "I am writing a research paper on a idea Spectral transition measure for detection of obstruents"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Obstruents are very important acoustical events (i.e., abrupt-consonantal landmarks) in the speech signal.  presents the use of novel Spectral Transition Measure (STM) to locate the obstruents in the continuous speech signal. The problem of obstruent detection involves detection of phonetic boundaries associated with obstruent sounds. ,  propose use of STM information derived from state-of-the-art Mel Frequency Cepstral Coefficients (MFCC) feature set and newly developed feature set, viz., MFCC-TMP (which uses Teager Energy Operator (TEO) to exploit implicitly Magnitude and Phase information in the MFCC framework) for obstruent detection. The key idea here is to exploit capabilities of STM to capture high dynamic transitional characteristics associated with obstruent sounds. The experimental setup is developed on entire TIMIT database. For 20 ms agreement (tolerance) duration, obstruent detection rate is found to be 97.59 % with 17.65 % false acceptance using state-of-the-art MFCC-STM and 96.42 % with 12.88 % false acceptance using MFCC-TMP-STM. Finally, STM-based features along with static representation (i.e., MFCC-STM and MFCC-TMP-STM) are evaluated for phone recognition task."}]