Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In natural speech, some phoneme transitions correspond to abrupt changes in the acoustic signal. Others are less clear-cut because the acoustic transition from one phoneme to the next is gradual.   determine the naturally occurring groups of phonemes (regardless of conventional phonetic categories) which show similar characteristics in such behavior. These data-driven groupings could be used in the design of decision-trees for context-dependent phoneme clustering, as used in large-vocabulary speech recognition and alignment systems, or during the design of speech databases for speech synthesis systems.  use 128 different Hidden Markov Model phoneme alignment systems and a large corpus of British English speech to assess the consistency with which different phoneme transitions can be identified. The phoneme transitions are grouped automatically so as to minimize the statistical differences in behavior beten members of each group. In this way  derive two sets of phonemic classes, one for the first phoneme of each phoneme-to-phoneme transition, and another for the second. The grouping of the phonemes confirms that broad phonetic classes are a significant indicator of the accuracy with which boundaries can be identified, but there are a number of exceptions and some apparent sub-divisions and mergers of accepted phonetic classes. The automatic grouping of the second phonemes results in two singletons, /Z/ and /N/ (in SAMPA notation). Finally, statistics are presented which characterize the precision with which transitions beten these automatic classes can be identified. These could provide ightings to be applied to different transitions to provide a more realistic assessment when evaluating the relative accuracies of different alignment systems.
['Ladan Baghai-Ravary']
I am writing a research paper on a idea The Inherent Temporal Precision of Phoneme Transitions
[{"from": "human", "value": "I am writing a research paper on a idea The Inherent Temporal Precision of Phoneme Transitions"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In natural speech, some phoneme transitions correspond to abrupt changes in the acoustic signal. Others are less clear-cut because the acoustic transition from one phoneme to the next is gradual.   determine the naturally occurring groups of phonemes (regardless of conventional phonetic categories) which show similar characteristics in such behavior. These data-driven groupings could be used in the design of decision-trees for context-dependent phoneme clustering, as used in large-vocabulary speech recognition and alignment systems, or during the design of speech databases for speech synthesis systems.  use 128 different Hidden Markov Model phoneme alignment systems and a large corpus of British English speech to assess the consistency with which different phoneme transitions can be identified. The phoneme transitions are grouped automatically so as to minimize the statistical differences in behavior beten members of each group. In this way  derive two sets of phonemic classes, one for the first phoneme of each phoneme-to-phoneme transition, and another for the second. The grouping of the phonemes confirms that broad phonetic classes are a significant indicator of the accuracy with which boundaries can be identified, but there are a number of exceptions and some apparent sub-divisions and mergers of accepted phonetic classes. The automatic grouping of the second phonemes results in two singletons, /Z/ and /N/ (in SAMPA notation). Finally, statistics are presented which characterize the precision with which transitions beten these automatic classes can be identified. These could provide ightings to be applied to different transitions to provide a more realistic assessment when evaluating the relative accuracies of different alignment systems."}]