Applications of Positive Time-Frequency Distributions to Speech Processing

Much of our current knowledge and intuition of speech is derived from analyses involving assumptions of short-time stationarity (e. g., the speech spectrogram). Such methods are, by their very nature, incapable of revealing the true nonstationary nature of speech. A careful consideration of the theory of time-frequency distributions (TFD's), however, allows the construction of methods that reveal far more of the nonstationarities of speech, thereby highlighting just what it is that conventional approaches miss. We apply two iterative methods for generating positive time-frequency distributions (TFD's) to speech analysis. Both methods make use of multiple sources of information (e. g., multiple spectrograms) to yield a high-resolution estimate of the joint time-frequency energy density of speech. Plosive events and formant harmonic structure are simultaneously preserved in these TFD's. Rapidly time-varying formants are also resolved by these TFD's, and harmonic structure is revealed, independent of sweep rate; this result is quite different from that seen with conventional speech spectrograms. The speech features observed in these distributions demonstrate that conventional sliding window techniques lose or distort much of the rich nonstationary structure of speech. Examples for synthetic formants and real speech are provided. The differences between joint distributions and conditional distributions are also illustrated.