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An Introduction to text-to-speech synthesis

A Short Introduction to Text-to-Speech Synthesis

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ELECTRICAL ENGINEERING - University of Washington

The main deficiency of the ordinary LP method is that it represents an all-pole model, which means that phonemes that contain antiformants such as nasals and nasalized vowels are poorly modeled. The quality is also poor with short plosives because the time-scale events may be shorter than the frame size used for analysis. With these deficiencies the speech synthesis quality with standard LPC method is generally considered poor, but with some modifications and extensions for the basic model the quality may be increased.

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A continuing goal must be to understand how linguistic structure manifests itself in the acoustic waveform of speech. Learning how to represent phonetic elements, syllables, stress, emphasis, etc., in a form that can be effectively coupled to speech modeling, analysis, and synthesis techniques should continue to have high priority in speech research. Increased knowledge in this area is obviously essential for text-to-speech synthesis, where the goal is to ensure that linguistic structure is correctly introduced into the synthetic waveform, but more effective application of this knowledge in speech analysis techniques could lead to much improved analysis/synthesis coders as well.

In using digital speech coding and synthesis for voice response from machines, the following four considerations lead to a wide range of trade-off configurations: (1) complexity of analysis/synthesis operations, (2) bit rate, (3) perceived quality, and (4) flexibility to modify or make new utterances. Clearly, straightforward playback of sampled and quantized speech is the simplest approach, requiring the highest bit rate for good quality and offering almost no flexibility other than that of simply splicing waveforms of words and phrases together to make new utterances. Therefore, this approach is usually only attractive where a fixed and manageable number of utterances is required. At the other extreme is text-to-speech synthesis, which, for a single investment in program, dictionary, and rule base storage, offers virtually unlimited flexibility to synthesize speech utterances. Here the text-to-speech algorithm may require significant computational resources. The usability and perceived quality of text-to-synthetic speech has progressed from barely intelligible and "machine-like" in the early days of synthesis research to highly intelligible and only slightly unnatural today. This has been achieved with a variety of approaches ranging from concatenation of diphone elements of natural speech represented in analysis/synthesis form to pure computation of synthesis parameters for physical models of speech production.

Mathematical and Natural Sciences

where is a predicted value, is the linear predictor order, and are the linear prediction coefficients which are found by minimizing the sum of the squared errors over a frame. Two methods, the covariance method and the autocorrelation method, are commonly used to calculate these coefficients. Only with the autocorrelation method the filter is guaranteed to be stable (Witten 1982, Kleijn et al. 1998).In synthesis phase the used excitation is approximated by a train of impulses for voiced sounds and by random noise for unvoiced. The excitation signal is then gained and filtered with a digital filter for which the coefficients are . The filter order is typically between 10 and 12 at 8 kHz sampling rate, but for higher quality at 22 kHz sampling rate, the order needed is between 20 and 24 (Kleijn et al. 1998, Karjalainen et al. 1998). The coefficients are usually updated every 5-10 ms.The main deficiency of the ordinary LP method is that it represents an all-pole model, which means that phonemes that contain antiformants such as nasals and nasalized vowels are poorly modeled. The quality is also poor with short plosives because the time-scale events may be shorter than the frame size used for analysis. With these deficiencies the speech synthesis quality with standard LPC method is generally considered poor, but with some modifications and extensions for the basic model the quality may be increased.Warped Linear Prediction (WLP) takes advantages of human hearing properties and the needed order of filter is then reduced significally, from orders 20-24 to 10-14 with 22 kHz sampling rate (Laine et al. 1994, Karjalainen et al. 1998). The basic idea is that the unit delays in digital filter are replaced by following all-pass sections(5.4)

recognition system. The "front-end" processing extracts a parametric representation or input pattern from the digitized input speech signal using the same types of techniques (e.g., linear predictive analysis or filter banks) that are used in speech analysis/synthesis systems. These acoustic features are designed to capture the linguistic features in a form that facilitates accurate linguistic decoding of the utterance. Cepstrum coefficients derived from either LPC parameters or spectral amplitudes derived from FFT or filter bank outputs are widely used as features (Rabiner and Juang, 1993). Such analysis techniques are often combined with vector quantization to provide a compact and effective feature representation. At the heart of a speech recognition system is the set of algorithms that compare the feature pattern representation of the input to members of a set of stored reference patterns that have been obtained by a training process. Equally important are algorithms for making a decision about the pattern to which the input is closest. Cepstrum distance measures are widely used for comparison of feature vectors, and dynamic time warping (DTW) and hidden Markov models (HMMs) have been shown to be very effective in dealing with the variability of speech (Rabiner and Juang, 1993). As shown in Figure 7, the most sophisticated systems also employ grammar and language models to aid in the decision process.

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Scientific FAQs | Dr. Caroline Leaf

We can position the different synthesis methods along a ''knowledge about speech" scale. Obviously, articulatory synthesis needs considerable understanding of the speech act itself, while models based on coding use such knowledge only to a limited extent. All synthesis methods have to model something that is partly unknown. Unfortunately, artificial obstacles due to simplifications or lack of coverage will also be introduced. A trend in current speech technology, both in speech understanding and speech production, is to avoid explicit formulation of knowledge and to use automatic methods to aid the development of the system. Since such analysis methods lack the human ability to generalize, the generalization has to be present in the data itself. Thus, these methods need large amounts of speech data. Models working close to the waveform are now typically making use of increased unit sizes while still modeling prosody by rule. In the middle of the scale, "formant synthesis" is moving toward the articulatory models by looking for "higher-level parameters" or to larger prestored units. Articulatory synthesis, hampered by lack of data, still has some way to go but is yielding improved quality, due mostly to advanced analysis-synthesis techniques.

Read about Dr. Leaf's scientific frequently asked questions.

In efforts to reduce the bit rate, an additional trade-off comes into play—that is, the complexity of the analysis/synthesis modeling processes. In general, any attempt to lower the bit rate while maintaining high quality will increase the complexity (and computational load) of the analysis and synthesis operations. At present, toll quality analysis/ synthesis representations can be obtained at about 8000 bits/second or an average of about one bit per sample (see Flanagan, in this volume). Attempting to lower the bit rate further leads to degradation in the quality of the reconstructed signal; however, intelligible speech can be reproduced with bit rates as low as 2000 bits/second (see Flanagan, in this volume).

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