EP1501075A2 - Sprachsynthese mittels Verknüpfung von Sprachwellenformen - Google Patents

Sprachsynthese mittels Verknüpfung von Sprachwellenformen Download PDF

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Publication number
EP1501075A2
EP1501075A2 EP04077723A EP04077723A EP1501075A2 EP 1501075 A2 EP1501075 A2 EP 1501075A2 EP 04077723 A EP04077723 A EP 04077723A EP 04077723 A EP04077723 A EP 04077723A EP 1501075 A2 EP1501075 A2 EP 1501075A2
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Prior art keywords
speech
waveform
database
waveforms
phoneme
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EP04077723A
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English (en)
French (fr)
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EP1501075A3 (de
EP1501075B1 (de
Inventor
Geert Coorman
Mario De Brock
Filip Deprez
Justin Fackrell
Steven Leys
Peter Rutten
Jan Demoortel
Andre Schenk
Bert Van Coile
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Lernout and Hauspie Speech Products NV
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Lernout and Hauspie Speech Products NV
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Priority claimed from EP99972346A external-priority patent/EP1138038B1/de
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    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L13/00—Speech synthesis; Text to speech systems
    • G10L13/06—Elementary speech units used in speech synthesisers; Concatenation rules
    • G10L13/07—Concatenation rules

Definitions

  • the present invention relates to a speech synthesizer based on concatenation of digitally sampled speech units from a large database of such samples and associated phonetic, symbolic, and numeric descriptors.
  • a concatenation-based speech synthesizer uses pieces of natural speech as building blocks to reconstitute an arbitrary utterance.
  • a database of speech units may hold speech samples taken from an inventory of pre-recorded natural speech data. Using recordings of real speech preserves some of the inherent characteristics of a real person's voice. Given a correct pronunciation, speech units can then be concatenated to form arbitrary words and sentences.
  • An advantage of speech unit concatenation is that it is easy to produce realistic coarticulation effects, if suitable speech units are chosen. It is also appealing in terms of its simplicity, in that all knowledge concerning the synthetic message is inherent to the speech units to be concatenated. Thus, little attention needs to be paid to the modeling of articulatory movements. However speech unit concatenation has previously been limited in usefulness to the relatively restricted task of neutral spoken text with little, if any, variations in inflection.
  • a tailored corpus is a well-known approach to the design of a speech unit database in which a speech unit inventory is carefully designed before making the database recordings.
  • the raw speech database then consists of carriers for the needed speech units.
  • This approach is well-suited for a relatively small footprint speech synthesis system.
  • the main goal is phonetic coverage of a target language, including a reasonable amount of coarticulation effects.
  • No prosodic variation is provided by the database, and the system instead uses prosody manipulation techniques to fit the database speech units into a desired utterance.
  • Coarticulation problems can be minimized by choosing an alternative unit.
  • One popular unit is the diphone, which consists of the transition from the center of one phoneme to the center of the following one. This model helps to capture transitional information between phonemes. A complete set of diphones would number approximately 1600, since there are approximately (40) 2 possible combinations of phoneme pairs. Diphone speech synthesis thus requires only a moderate amount of storage.
  • One disadvantage of diphones is that they lead to a large number of concatenation points (one per phoneme), so that heavy reliance is placed upon an efficient smoothing algorithm, preferably in combination with a diphone boundary optimization.
  • Traditional diphone synthesizers such as the ITS-3000 of Lernout & Hauspie Speech And Language Products N.V., use only one candidate speech unit per diphone. Due to the limited prosodic variability, pitch and duration manipulation techniques are needed to synthesize speech messages. In addition, diphones synthesis does not always result in good output speech quality.
  • Syllables have the advantage that most coarticulation occurs within syllable boundaries. Thus, concatenation of syllables generally results in good quality speech.
  • One disadvantage is the high number of syllables in a given language, requiring significant storage space.
  • demi-syllables were introduced. These half-syllables, are obtained by splitting syllables at their vocalic nucleus.
  • the syllable or demi-syllable method does not guarantee easy concatenation at unit boundaries because concatenation in a voiced speech unit is always more difficult that concatenation in unvoiced speech units such as fricatives.
  • the first speech synthesizer of this kind was presented in Sagisaka, Y., "Speech synthesis by rule using an optimal selection of non-uniform synthesis units," ICASSP-88 New York vol.1 pp. 679-682, IEEE, April 1988. It uses a speech database and a dictionary of candidate unit templates, i.e. an inventory of all phoneme sub-strings that exist in the database. This concatenation-based synthesizer operates as follows.
  • Step (3) is based on an appropriateness measure - taking into account four factors: conservation of consonant-vowel transitions, conservation of vocalic sound succession, long unit preference, overlap between selected units.
  • the system was developed for Japanese, the speech database consisted of 5240 commonly used words.
  • the annotation of the database is more refined than was the case in the Sagisaka system: apart from phoneme identity there is an annotation of phoneme class, source utterance, stress markers, phoneme boundary, identity of left and right context phonemes, position of the phoneme within the syllable, position of the phoneme within the word, position of the phoneme within the utterance, pitch peak locations.
  • Speech unit selection in the SpeakEZ is performed by searching the database for phonemes that appear in the same context as the target phoneme string.
  • a penalty for the context match is computed as the difference between the immediately adjacent phonemes surrounding the target phoneme with the corresponding phonemes adjacent to the database phoneme candidate.
  • the context match is also influenced by the distance of the phoneme to its left and right syllable boundary, left and right word boundary, and to the left and right utterance boundary.
  • Speech unit waveforms in the SpeakEZ are concatenated in the time domain, using pitch synchronous overlap-add (PSOLA) smoothing between adjacent phonemes.
  • PSOLA pitch synchronous overlap-add
  • phonetic context In continuity distortion, three features are used: phonetic context, prosodic context, and acoustic join cost.
  • Phonetic and prosodic context distances are calculated between selected units and the context (database) units of other selected. units.
  • the acoustic join cost is calculated between two successive selected units. The acoustic join cost is based on a quantization of the mel-cepstrum, calculated at the best joining point around the labeled boundary.
  • a Viterbi search is used to find the path with the minimum cost as expressed in (3).
  • An exhaustive search is avoided by pruning the candidate lists at several stages in the selection process. Units are concatenated without doing any signal processing (i.e., raw concatenation).
  • a clustering technique is presented in Black, A.W., Taylor, P.," Automatically clustering similar units for unit selection in speech synthesis,” Proc. Eurospeech '97, Rhodes, pp. 601-604, 1997, that creates a CART (classification and regression tree) for the units in the database.
  • the CART is used to limit the search domain of candidate units, and the unit distortion cost is the distance between the candidate unit and its cluster center.
  • a speech synthesizer comprising:
  • the polyphone designators are diphone designators.
  • the synthesizer also includes (i) a digital storage medium in which the speech waveforms are stored in speech-encoded form; and (ii) a decoder that decodes the encoded speech waveforms when accessed by the waveform selector.
  • the synthesizer operates to select among waveform candidates without recourse to specific target duration values or specific target pitch contour values over time.
  • a speech synthesizer using a context-dependent cost function includes:
  • a speech synthesizer with a context-dependent cost function includes:
  • a speech synthesizer in a further embodiment, there is provided a speech synthesizer, and the embodiment provides:
  • a speech synthesizer includes:
  • Another embodiment provides a speech synthesizer, and the embodiment includes:
  • the phase match is achieved by changing the location only of the leading edge and by changing the location only of the trailing edge.
  • the optimization is determined on the basis of similarity in shape of the first and second waveforms in the regions near the locations.
  • similarity is determined using a cross-correlation technique, which optionally is normalized cross correlation.
  • the optimization is determined using at least one non-rectangular window.
  • the optimization is determined in a plurality of successive stages in which time resolution associated with the first and second waveforms is made successively finer.
  • the change in resolution is achieved by downsampling.
  • a representative embodiment of the present invention known as the RealSpeakTM Text-to-Speech (TTS) engine, produces high quality speech from a phonetic specification, that can be the output of a text processor, known as a target, by concatenating parts of real recorded speech held in a large database.
  • the main process objects that make up the engine, as shown in Fig. 1, include a text processor 101, a target generator 111, a speech unit database 141, a waveform selector 131, and a speech waveform concatenator 151.
  • the speech unit database 141 contains recordings, for example in a digital format such as PCM, of a large corpus of actual speech that are indexed in individual speech units by their phonetic descriptors, together with associated speech unit descriptors of various speech unit features.
  • speech units in the speech unit database 141 are in the form of a diphone, which starts and ends in two neighboring phonemes.
  • Other embodiments may use differently sized and structured speech units.
  • Speech unit descriptors include, for example, symbolic descriptors e.g ., lexical stress, word position, etc. ⁇ and prosodic descriptors e . g . duration, amplitude, pitch, etc.
  • the text processor 101 receives a text input, e.g ., the text phrase "Hello, goodbye! The text phrase is then converted by the text processor 101 into an input phonetic data sequence.
  • this is a simple phonetic transcription ⁇ #'hE-10#'Gud-bY#.
  • the input phonetic data sequence may be in one of various different forms.
  • the input phonetic data sequence is converted by the target generator 111 into a multi-layer internal data sequence to be synthesized.
  • This internal data sequence representation known as extended phonetic transcription (XPT), includes phonetic descriptors, symbolic descriptors, and prosodic descriptors such as those in the speech unit database 141.
  • the waveform selector 131 retrieves from the speech unit database 141 descriptors of candidate speech units that can be concatenated into the target utterance specified by the XPT transcription.
  • the waveform selector 131 creates an ordered list of candidate speech units by comparing the XPTs of the candidate speech units with the XPT of the target XPT, assigning a node cost to each candidate.
  • Candidate-to-target matching is based on symbolic descriptors,such as phonetic context and prosodic context, and numeric descriptors and determines how well each candidate fits the target specification. Poorly matching candidates may be excluded at this point.
  • the waveform selector 131 determines which candidate speech units can be concatenated without causing disturbing quality degradations such as clicks, pitch discontinuities, etc. Successive candidate speech units are evaluated by the waveform selector 131 according to a quality degradation cost function. Candidate-to-candidate matching uses frame-based information such as energy, pitch and spectral information to determine how well the candidates can be joined together. Using dynamic programming, the best sequence of candidate speech units is selected for output to the speech waveform concatenator 151.
  • the speech waveform concatenator 151 requests the output speech units (diphones and/or polyphones) from the speech unit database 141 for the speech waveform concatenator 151.
  • the speech waveform concatenator 151 concatenates the speech units selected forming the output speech that represents the target input text.
  • the speech unit database 141 contains three types of files:
  • Each diphone is identified by two phoneme symbols - these two symbols are the key to the diphone lookup table 63.
  • a diphone index table 631 contains an entry for each possible diphone in the language, describing where the references of these diphones can be found in the diphone reference table 632.
  • the diphone reference table 632 contains references to all the diphones in the speech unit database 141. These references are alphabetically ordered by diphone identifier. In order to reference all diphones by identity it is sufficient to specify where a list starts in the diphone lookup table 63, and how many diphones it contains.
  • Each diphone reference contains the number of the message (utterance) where it is found in the speech unit database 141, which phoneme the diphone starts at, where the diphone starts in the speech signal, and the duration of the diphone.
  • a significant factor for the quality of the system is the transcription that is used to represent the speech signals in the speech unit database 141.
  • Representative embodiments set out to use a transcription that will allow the system to use the intrinsic prosody in the speech unit database 141 without requiring precise pitch and duration targets. This means that the system can select speech units that are matched phonetically and prosodically to an input transcription. The concatenation of the selected speech units by the speech waveform concatenator 151 effectively leads to an utterance with the desired prosody.
  • the XPT contains two types of data: symbolic features (i.e., features that can be derived from text) and acoustic features (i.e., features that can only be derived from the recorded speech waveform).
  • the XPT typically contains a time aligned phonetic description of the utterance. The start of each phoneme in the signal is included in the transcription;
  • the XPT also contains a number of prosody related cues, e.g., accentuation and position information.
  • the transcription also contains acoustic information related to prosody, e.g. the phoneme duration.
  • a typical embodiment concatenates speech units from the speech unit database 141 without modification of their prosodic or spectral realization.
  • the boundaries of the speech units should have matching spectral and prosodic realizations.
  • the necessary information required to verify this match is typically incorporated into the XPT by a boundary pitch value and spectral data.
  • the boundary pitch value and the spectrum are calculated at the polyphone edges.
  • Different types of data in the speech unit database 141 may be stored on different physical media, e . g ., hard disk, CD-ROM, DVD, random-access memory (RAM), etc.
  • Data access speed may be increased by efficiently choosing how to distribute the data between these various media.
  • the slowest accessing component of a computer system is typically the hard disk. If part of the speech unit information needed to select candidates for concatenation were stored on such a relatively slow mass storage device, valuable processing time would be wasted by accessing this slow device. A much faster implementation could be obtained if selection-related data were stored in RAM.
  • the speech unit database 141 is partitioned into frequently needed selection-related data 21 ⁇ stored in RAM, and less frequently needed concatenation-related data 22 ⁇ stored, for example, on CD-ROM or DVD.
  • RAM requirements of the system remain modest, even if the amount of speech data in the database becomes extremely large ( ⁇ Gbytes).
  • the relatively small number of CD-ROM retrievals may accommodate multi-channel applications using one CD-ROM for multiple threads, and the speech database may reside alongside other application data on the CD (e.g ., navigation systems for an auto-PC).
  • speech waveforms may be coded and/or compressed using techniques well-known in the art.
  • each candidate list in the waveform selector 131 contains many available matching diphones in the speech unit database 141 . Matching here means merely that the diphone identities match. Thus in an example of a diphone'#1' in which the initial '1' has primary stress in the target, the candidate list in the waveform selector 131 contains every '#1' found in the speech unit database 141 , including the ones with unstressed or secondary stressed '1'.
  • the waveform selector 131 uses Dynamic Programming (DP) to find the best sequence of diphones so that:
  • DP Dynamic Programming
  • the cost functions used in the unit selection may be of two types depending on whether the features involved are symbolic (i . e ., non numeric e.g. , stress, prominence, phoneme context) or numeric (e . g ., spectrum, pitch, duration).
  • the simplest cost weight function would be a binary 0/1. If the candidate has the same value as the target, then the cost is 0; if the candidate is something different, then the cost is 1. For example, when scoring a candidate for its stress (sentence accent (strongest), primary, secondary, unstressed (weakest)) for a target with the strongest stress, this simple system would score primary, secondary or unstressed candidates with a cost of 1. This is counter-intuitive, since if the target is the strongest stress, a candidate of primary stress is preferable to a candidate with no stress.
  • the user can set up tables which describe the cost between any 2 values of a particular symbolic feature. Some examples are shown in Table 1 and Table 2 in the Tables Appendix which are called 'fuzzy tables' because they resemble concepts from fuzzy logic. Similar tables can be set up for any or all of the symbolic features used in the NodeCost calculation.
  • Fuzzy tables in the waveform selector 131 may also use special symbols, as defined by the developer linguist, which mean 'BAD' and 'VERY BAD'.
  • the linguist puts a special symbol /1 for BAD, or /2 for VERY BAD in the fuzzy table, as shown in Table 1 in the Tables Appendix, for a target prominence of 3 and a candidate prominence of 0. It was previously mentioned that the normal minimum contribution from any feature is 0 and the maximum is 1. By using /1 or /2 the cost of feature mismatch can be made much higher than 1, such that the candidate is guaranteed to get a high cost.
  • the input specification is used to symbolically choose the best combination of speech units from the database which match the input specification.
  • using fixed cost functions for symbolic features to decide which speech units are best, ignores well-known linguistic phenomena such as the fact that some symbolic features are more important in certain contexts than others.
  • the speech unit selection strategy offers several scaling possibilities.
  • the waveform selector 131 retrieves speech unit candidates from the speech unit database 141 by means of lookup tables that speed up data retrieval.
  • the input key used to access the lookup tables represents one scalability factor.
  • This input key to the lookup table can vary from minimal ⁇ e . g ., a pair of phonemes describing the speech unit core ⁇ to more complex ⁇ e . g ., a pair of phonemes + speech unit features (accentuation, context,).
  • a more complex the input key results in fewer candidate speech units being found through the lookup table.
  • smaller (although not necessarily better) candidate lists are produced at the cost of more complex lookup tables.
  • the size of the speech unit database 141 is also a significant scaling factor, affecting both required memory and processing speed.
  • the minimal database needed consists of isolated speech units that cover the phonetics of the input (comparable to the speech data bases that are used in linear predictive coding-based phonetics-to-speech systems). Adding well chosen speech signals to the database, improves the quality of the output speech at the cost of increasing system requirements.
  • the pruning techniques described above also represents a scalability factor which can speed up unit selection.
  • a further scalability factor relates to the use of a speech coding and/or speech compression techniques to reduce the size of the speech database.
  • the speech waveform concatenator 151 performs concatenation-related signal processing.
  • the synthesizer generates speech signals by joining high-quality speech segments together. Concatenating unmodified PCM speech waveforms in the time domain has the advantage that the intrinsic segmental information is preserved. This implies also that the natural prosodic information, including the micro-prosody, is transferred to the synthesized speech. Although the intra-segmental acoustic quality is optimal, attention should be paid to the waveform joining process that may cause inter-segmental distortions.
  • the major concern of waveform concatenation is in avoiding waveform irregularities such as discontinuities and fast transients that may occur in the neighborhood of the join. These waveform irregularities are generally referred to as concatenation artifacts.
  • the concatenation of two segments can be performed by using the well-known weighted overlap-and-add (OLA) method.
  • OVA overlap-and-add
  • the overlap and-add procedure for segment concatenation is in fact nothing else than a (non-linear) short time fade-in/fade-out of speech segments.
  • To get high-quality concatenation we locate a region in the trailing part of the first segment and we locate a region in the leading part of the second segment, such that a phase mismatch measure between the two regions is minimized. This process is performed as follows:
  • Representative embodiments can be implemented as a computer program product for use with a computer system.
  • Such implementation may include a series of computer instructions fixed either on a tangible medium, such as a computer readable medium (e . g ., a diskette, CD-ROM, ROM, or fixed disk) or transmittable to a computer system, via a modem or other interface device, such as a communications adapter connected to a network over a medium.
  • the medium may be either a tangible medium (e.g ., optical or analog communications lines) or a medium implemented with wireless techniques (e.g ., microwave, infrared or other transmission techniques).
  • the series of computer instructions embodies all or part of the functionality previously described herein with respect to the system.
  • Such computer instructions can be written in a number of programming languages for use with many computer architectures or operating systems. Furthermore, such instructions may be stored in any memory device, such as semiconductor, magnetic, optical or other memory devices, and may be transmitted using any communications technology, such as optical, infrared, microwave, or other transmission technologies. It is expected that such a computer program product may be distributed as a removable medium with accompanying printed or electronic documentation (e . g ., shrink wrapped software), preloaded with a computer system ( e . g ., on system ROM or fixed disk), or distributed from a server or electronic bulletin board over the network ( e . g ., the Internet or World Wide Web).
  • printed or electronic documentation e . g ., shrink wrapped software
  • preloaded with a computer system e . g ., on system ROM or fixed disk
  • server or electronic bulletin board e . g ., the Internet or World Wide Web
  • embodiments of the invention may be implemented as a combination of both software (e . g ., a computer program product) and hardware. Still other embodiments of the invention are implemented as entirely hardware, or entirely software ( e . g ., a computer program product).

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  • Engineering & Computer Science (AREA)
  • Computational Linguistics (AREA)
  • Health & Medical Sciences (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Human Computer Interaction (AREA)
  • Physics & Mathematics (AREA)
  • Acoustics & Sound (AREA)
  • Multimedia (AREA)
  • Machine Translation (AREA)
  • Reduction Or Emphasis Of Bandwidth Of Signals (AREA)
EP04077723A 1998-11-13 1999-11-12 Sprachsynthese mittels Verknüpfung von Sprachwellenformen Expired - Lifetime EP1501075B1 (de)

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EP99972346A EP1138038B1 (de) 1998-11-13 1999-11-12 Sprachsynthese durch verkettung von sprachwellenformen

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DE69428612T2 (de) * 1993-01-25 2002-07-11 Matsushita Electric Industrial Co., Ltd. Verfahren und Vorrichtung zur Durchführung einer Zeitskalenmodifikation von Sprachsignalen
JP3349905B2 (ja) * 1996-12-10 2002-11-25 松下電器産業株式会社 音声合成方法および装置
JP3644263B2 (ja) * 1998-07-31 2005-04-27 ヤマハ株式会社 波形形成装置及び方法

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
BANGA; GARCIA MATEO: "Shape invariant pitch-synchronous text-to-speech conversion", ICASSP 90, 1990
BLACK, A.W.; TAYLOR, P.: "Automatically clustering similar units for unit selection in speech synthesis", PROC. EUROSPEECH '97, 1997, pages 601 - 604
DING, W.; CAMPBELL, N.: "Optimising unit selection with voice source and formants in the CHATR speech synthesis system", PROC. EUROSPEECH '97, 1997, pages 537 - 540
HUNT; BLACK: "Unit selection in a concatenative speech synthesis system using a large speech database", IEEE, 1996

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EP1501075B1 (de) 2009-04-15

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