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The speaker's feelings and emotional state affect speech in many ways and the proper implementation of these features in synthesized speech may increase the quality considerably. With text-to-speech systems this is rather difficult because written text usually contains no information of these features. However, this kind of information may be provided to a synthesizer with some specific control characters or character strings. These methods are described later in Chapter 7. The users of speech synthesizers may also need to express their feelings in "real-time". For example, deafened people can not express their feelings when communicating with speech synthesizer through a telephone line. Emotions may also be controlled by specific software to control synthesizer parameters. Such system is for example HAMLET (Helpful Automatic Machine for Language and Emotional Talk) which drives the commercial DECtalk synthesizer (Abadjieva et al. 1993, Murray et al. 1996).

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Diphones (or dyads) are defined to extend the central point of the steady state part of the phone to the central point of the following one, so they contain the transitions between adjacent phones. That means that the concatenation point will be in the most steady state region of the signal, which reduces the distortion from concatenation points. Another advantage with diphones is that the coarticulation effect needs no more to be formulated as rules. In principle, the number of diphones is the square of the number of phonemes (plus allophones), but not all combinations of phonemes are needed. For example, in Finnish the combinations, such as /hs/, /sj/, /mt/, /nk/, and / p/ within a word are not possible. The number of units is usually from 1500 to 2000, which increases the memory requirements and makes the data collection more difficult compared to phonemes. However, the number of data is still tolerable and with other advantages, diphone is a very suitable unit for sample-based text-to-speech synthesis. The number of diphones may be reduced by inverting symmetric transitions, like for example /as/ from /sa/.

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Building the unit inventory consists of three main phases (Hon et al. 1998). First, the natural speech must be recorded so that all used units (phonemes) within all possible contexts (allophones) are included. After this, the units must be labeled or segmented from spoken speech data, and finally, the most appropriate units must be chosen. Gathering the samples from natural speech is usually very time-consuming. However, some is this work may be done automatically by choosing the input text for analysis phase properly. The implementation of rules to select correct samples for concatenation must also be done very carefully.

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Analysis for correct pronunciation from written text has also been one of the most challenging tasks in speech synthesis field. Especially, with some telephony applications where almost all words are common names or street addresses. One method is to store as much names as possible into a specific pronunciation table. Due to the amount of excisting names, this is quite unreasonable. So rule-based system with an exception dictionary for words that fail with those letter-to-phoneme rules may be a much more reasonable approach (Belhoula et al. 1993). This approach is also suitable for normal pronunciation analysis. With morphemic analysis, a certain word can be divided in several independed parts which are considered as the minimal meaningful subpart of words as prefix, root, and affix. About 12 000 morphemes are needed for covering 95 percent of English (Allen et al.1987). However, the morphemic analysis may fail with word pairs, such as heal/health or sign/signal (Klatt 1987).

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where the "rules" may contain information of in which cases the current abbreviation is converted, e.g., if it is accepted in capitalized form or accepted with period or colon. Preceding and following information may contain also the accepted forms of ambient text, such as numbers, spaces, and character characteristics (vowel/consonant, capitalized etc.).Sometimes different special modes, especially with numbers, are used to make this stage more accurate, for example, math mode for mathematical expressions and date mode for dates and so on. Another situation where the specific rules are needed is for example the E-mail messages where the header information needs special attention.Analysis for correct pronunciation from written text has also been one of the most challenging tasks in speech synthesis field. Especially, with some telephony applications where almost all words are common names or street addresses. One method is to store as much names as possible into a specific pronunciation table. Due to the amount of excisting names, this is quite unreasonable. So rule-based system with an exception dictionary for words that fail with those letter-to-phoneme rules may be a much more reasonable approach (Belhoula et al. 1993). This approach is also suitable for normal pronunciation analysis. With morphemic analysis, a certain word can be divided in several independed parts which are considered as the minimal meaningful subpart of words as prefix, root, and affix. About 12 000 morphemes are needed for covering 95 percent of English (Allen et al.1987). However, the morphemic analysis may fail with word pairs, such as heal/health or sign/signal (Klatt 1987).Another perhaps relatively good approach to the pronunciation problem is a method called where a novel word is recognized as parts of the known words and the part pronunciations are built up to produce the pronunciation of a new word, for example pronunciation of word may be constructed from and (Gaved 1993). In some situations, such as speech markup languages described later in Chapter 7, information of correct pronunciation may be given separately.Prosodic or suprasegmental features consist of pitch, duration, and stress over the time. With good controlling of these gender, age, emotions, and other features in speech can be well modeled. However, almost everything seems to have effect on prosodic features of natural speech which makes accurate modeling very difficult. Prosodic features can be divided into several levels such as syllable, word, or phrase level. For example, at word level vowels are more intense than consonants. At phrase level correct prosody is more difficult to produce than at the word level.The pitch pattern or fundamental frequency over a sentence (intonation) in natural speech is a combination of many factors. The pitch contour depends on the meaning of the sentence. For example, in normal speech the pitch slightly decreases toward the end of the sentence and when the sentence is in a question form, the pitch pattern will raise to the end of sentence. In the end of sentence there may also be a continuation rise which indicates that there is more speech to come. A raise or fall in fundamental frequency can also indicate a stressed syllable (Klatt 1987, Donovan 1996). Finally, the pitch contour is also affected by gender, physical and emotional state, and attitude of the speaker.The duration or time characteristics can also be investigated at several levels from phoneme (segmental) durations to sentence level timing, speaking rate, and rhythm. The segmental duration is determined by a set of rules to determine correct timing. Usually some inherent duration for phoneme is modified by rules between maximum and minimum durations. For example, consonants in non-word-initial position are shortened, emphasized words are significantly lengthened, or a stressed vowel or sonorant preceded by a voiceless plosive is lengthened (Klatt 1987, Allen et al. 1987). In general, the phoneme duration differs due to neighboring phonemes. At sentence level, the speech rate, rhythm, and correct placing of pauses for correct phrase boundaries are important. For example, a missing phrase boundary just makes speech sound rushed which is not as bad as an extra boundary which can be confusing (Donovan 1996). With some methods to control duration or fundamental frequency, such as the PSOLA method, the manipulation of one feature affects to another (Kortekaas et al. 1997).The intensity pattern is perceived as a loudness of speech over the time. At syllable level vowels are usually more intense than consonants and at a phrase level syllables at the end of an utterance can become weaker in intensity. The intensity pattern in speech is highly related with fundamental frequency. The intensity of a voiced sound goes up in proportion to fundamental frequency (Klatt 1987). The speaker's feelings and emotional state affect speech in many ways and the proper implementation of these features in synthesized speech may increase the quality considerably. With text-to-speech systems this is rather difficult because written text usually contains no information of these features. However, this kind of information may be provided to a synthesizer with some specific control characters or character strings. These methods are described later in Chapter 7. The users of speech synthesizers may also need to express their feelings in "real-time". For example, deafened people can not express their feelings when communicating with speech synthesizer through a telephone line. Emotions may also be controlled by specific software to control synthesizer parameters. Such system is for example HAMLET (Helpful Automatic Machine for Language and Emotional Talk) which drives the commercial DECtalk synthesizer (Abadjieva et al. 1993, Murray et al. 1996).This section shortly introduces how some basic emotional states affect voice characteristics. The voice parameters affected by emotions are usually categorized in three main types (Abadjieva et al. 1993, Murray et al. 1993):The number of possible emotions is very large, but there are five discrete emotional states which are commonly referred as the primary or basic emotions and the others are altered or mixed forms of these (Abadjieva et al. 1993). These are anger, happiness, sadness, fear, and disgust. The secondary emotional states are for example whispering, shouting, grief, and tiredness. in speech causes increased intensity with dynamic changes (Scherer 1996). The voice is very breathy and has tense articulation with abrupt changes. The average pitch pattern is higher and there is a strong downward inflection at the end of the sentence. The pitch range and its variations are also wider than in normal speech and the average speech rate is also a little bit faster.

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Sinusoidal models are also used successfully in singing voice synthesis (Macon 1996, Macon et al. 1997). The synthesis of singing differs from speech synthesis in many ways. In singing, the intelligibility of the phonemic message is often secondary to the intonation and musical qualities. Vowels are usually sustained longer in singing than in normal speech, and naturally, easy and independent controlling of pitch and loudness is also required. The best known singing synthesis system is perhaps the LYRICOS which is developed at Georgia Institute of Technology. The system uses sinusoidal-modeled segments from an inventory of singing voice data collected from human vocalist maintaining the characteristics and perceived identity. The system uses a standard MIDI-interface where the user specifies a musical score, phonetically-spelled lyrics, and control parameters such as vibrato and vocal effort (Macon et al. 1997).