Comparison of parameter sets for temporal decomposition

Temporal decomposition of a speech utterance results in a description of speech parameters in terms of overlapping target functions and associated target factors. The former may correspond to articulatory gestures and the latter to ideal articulatory positions. Although developed for economical speech coding, this method also provides an interesting tool for deriving phonetic information from acoustic speech signals. The speech parameters used by Atal (1983) is proposing this method were the log-area parameters. Our modified temporal decomposition method (Van Dijk-Kappers and Marcus, 1987, 1989) also works with log-area parameters as input. However, the method is not restricted to these; in principle, most commonly used parameter sets can be used. In this paper we compare the results obtained with nine different sets of speech parametes, including log-area parameters, formants, reflection coefficients and band-filter parameters. The main criterion for good performance will be correspondence between target functions and phonemes or sub-phonemes. The phonetic relevance of the target vectors will also be considered, but in less detail. Speech signal resynthesis supplies yet another criterion; for those parameters sets which are transformable into the same parameter space, a reconstruction error will be defined and evaluated. From these experiments it can be concluded that log-area parameters from the most suitable parameter set available for temporal decomposition. In some respects band-filter parameters yield better results, but this set is not classified as the best due to properties related to resynthesi

Comparison of parameter sets for temporal decomposition

Temporal decomposition of a. speech utterance results in a description of speech panmeters in terms of overlapping target functions and associated target vectors. Although developed for economical speech coding, this method also provide! an interesting tool for deriving phonetic information from the acoustic speech signal. The target vectors may correspond to idealized articulatory targets; the target fun ctions describe the temporal evolution of these targets. The speech parameters used by Atal whe n he proposed this method (1983) are the log-area parameters. Our modified temporal decomposition method (1987) alro works with these parameters as input.. However, in principle, most commonly used parameter sets can be used. In t his paper we compare the results for six different sets of speech parameters . The main performance criterion will be the phonetic relevance of the target functions. The phonetic interpretation of the target vectors and the resynthesis of the speech signal will also be considered as criteria. From our experiments, we will conclude that the filter bank output para.meters and the log-area parameters are the most suitable parameter set! available for temporal decomposition

Comparison of parameter sets for temporal decomposition of speech

Temporal decomposition of a. speech utterance results in a description of speech panmeters in terms of overlapping target functions and associated target vectors. Although developed for economical speech coding, this method also provide! an interesting tool for deriving phonetic information from the acoustic speech signal. The target vectors may correspond to idealized articulatory targets; the target fun ctions describe the temporal evolution of these targets. The speech parameters used by Atal whe n he proposed this method (1983) are the log-area parameters. Our modified temporal decomposition method (1987) alro works with these parameters as input.. However, in principle, most commonly used parameter sets can be used. In t his paper we compare the results for six different sets of speech parameters . The main performance criterion will be the phonetic relevance of the target functions. The phonetic interpretation of the target vectors and the resynthesis of the speech signal will also be considered as criteria. From our experiments, we will conclude that the filter bank output para.meters and the log-area parameters are the most suitable parameter set! available for temporal decomposition