Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A new formant-tracking algorithm using phoneme information is proposed. Conventional formant-tracking algorithms obtain formant tracks by analyzing the acoustic speech signal using continuity constraints without any additional information. The formant-tracking error rate of the conventional methods is reportedly in the range of 10%-20%. ,  show that if text or phoneme transcription of speech utterances is available, the error rate can be significantly reduced. The basic idea behind this approach is that given the phoneme identity, formant-tracking algorithms can have a better clue of where to look for formants. The algorithm consists of three phases: 1) analysis, 2) segmentation and alignment, and 3) formant tracking by the Viterbi searching algorithm. In the analysis phase, formant candidates are obtained for each analysis frame by solving the linear prediction polynomial. In the segmentation and alignment phase, the text corresponding to the input speech utterance is converted into a sequence of phoneme symbols. Then, the phoneme sequence is time aligned with the speech utterance. A hidden Markov model (HMM) based automatic segmentation algorithm is used for forced-time alignment. For each phoneme segment, nominal formant frequencies are assigned at the center of each phoneme segment. Then nominal formant tracks for the entire utterance are obtained by interpolating the nominal formant frequencies. In order to compensate for the coarticulation effect, different interpolation methods are used depending on the phonemic context. The interpolation process makes the formant-tracking algorithm robust to possible segmentation errors made by the HMM-based segmentation algorithm. As a result, the proposed formant-tracking algorithm does not require highly accurate alignment/segmentation. Finally, a set of formants is chosen from the formant candidates in such a way that the resulting formant tracks come close to the nominal formant tracks while satisfying the continuity constraints. The algorithm is tested using natural speech utterances and the performance is compared against formant tracks obtained by the conventional method using continuity constraints only. The new algorithm significantly reduces the formant-tracking error rate (5.03% for male and 3.73% for female) over the conventional formant-tracking algorithm (13.00% for male and 15.82% for female).
['Minkyu Lee', 'J. van Santen', 'Bernd Möbius', 'Joseph P. Olive']
I am writing a research paper on a idea Formant tracking using context-dependent phonemic information
[{"from": "human", "value": "I am writing a research paper on a idea Formant tracking using context-dependent phonemic information"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A new formant-tracking algorithm using phoneme information is proposed. Conventional formant-tracking algorithms obtain formant tracks by analyzing the acoustic speech signal using continuity constraints without any additional information. The formant-tracking error rate of the conventional methods is reportedly in the range of 10%-20%. ,  show that if text or phoneme transcription of speech utterances is available, the error rate can be significantly reduced. The basic idea behind this approach is that given the phoneme identity, formant-tracking algorithms can have a better clue of where to look for formants. The algorithm consists of three phases: 1) analysis, 2) segmentation and alignment, and 3) formant tracking by the Viterbi searching algorithm. In the analysis phase, formant candidates are obtained for each analysis frame by solving the linear prediction polynomial. In the segmentation and alignment phase, the text corresponding to the input speech utterance is converted into a sequence of phoneme symbols. Then, the phoneme sequence is time aligned with the speech utterance. A hidden Markov model (HMM) based automatic segmentation algorithm is used for forced-time alignment. For each phoneme segment, nominal formant frequencies are assigned at the center of each phoneme segment. Then nominal formant tracks for the entire utterance are obtained by interpolating the nominal formant frequencies. In order to compensate for the coarticulation effect, different interpolation methods are used depending on the phonemic context. The interpolation process makes the formant-tracking algorithm robust to possible segmentation errors made by the HMM-based segmentation algorithm. As a result, the proposed formant-tracking algorithm does not require highly accurate alignment/segmentation. Finally, a set of formants is chosen from the formant candidates in such a way that the resulting formant tracks come close to the nominal formant tracks while satisfying the continuity constraints. The algorithm is tested using natural speech utterances and the performance is compared against formant tracks obtained by the conventional method using continuity constraints only. The new algorithm significantly reduces the formant-tracking error rate (5.03% for male and 3.73% for female) over the conventional formant-tracking algorithm (13.00% for male and 15.82% for female)."}]