Speech Analysis Speech

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1.7 Speech analysis One of the important characteristics of a speech waveform is the time-varying nature of the content of the speech pressure. Determination of the time-varying parameters of speech is a key area of analysis required in speech research. Another key area is classification of speech waveform segments into voiced or voiceless (mixed excitation is usually considered voiced). As mentioned previously, in the case where speech is voiced, the most important parameter is the fundamental frequency value f0. This section introduces these two areas of analysis and discusses the principles and limitation involved. First, the fundamental frequency f0 analysis is considered, followed by the spectral analysis method of dynamic speech…show more content…
This device produces a two-dimensional pattern called a spectrogram in which the vertical dimension corresponds to frequency and horizontal dimension to time. The 16 bit gray scale level is used to represent the given spectrogram. Even though the colour representation is more visually appealing, it sometimes leads to misleading interpretation of the spectrogram. The darkness of the pattern is proportional to signal energy. Thus, the resonance frequencies of the vocal tract show up as dark bands in the spectrogram .Voiced regions are characterized by a striated appearance due to the periodicity of the time waveform, while the unvoiced intervals more solidly filled in. An example, spectrogram of the utterance of “What do you think about that” of a female speaker (in the Figure 1.5a) is shown in the Figure 1.5b. The spectrogram is labelled corresponds to the labelling of Figure 1.5b, so that the time domain and frequency domain can be correlated. The time scale and frequency resolution of the spectrograph plays a vital role in representation of speech spectral energy. The most rapid changes in time scale occur during the release stages of plosives, which order is of 5-10ms. For individual representation of the harmonics of male speech, a frequency resolution less than the minimum expected f0 for males approximately 50…show more content…
This mixture is often confusing and inappropriate for further analysis, such as speech recognition. Ideally, some method of separating out the effects of the vocal tract and the excitation would be appropriate. Unfortunately, these two speech aspects are convolved together and they cannot be separated by simple filtering. One speech analysis approach that can help in separating the two elements is the Cepstrum [12]. This finds applications both pitch detection and vocal tract. The method relies on applying nonlinear operations to map the operation of convolution into a summation. Thus, signals which are convolved together are now signals simply added together. As a result, they can be readily separated, provided they do not overlap in this domain. This is achieved via two

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