Abstract:
A system and computer-readable medium are disclosed that synthesize speech from text using a triphone unit selection database. The instructions on the computer-readable medium control a computing device to perform the steps: receiving input text, selecting a plurality of N phoneme units from the triphone unit selection database as candidate phonemes for synthesized speech based on the input text, applying a cost process to select a set of phonemes from the candidate phonemes and synthesizing speech using the selected set of phonemes.

Description:
RELATED APPLICATIONS  
       [0001]     The present application is a continuation of U.S. patent application Ser. No. 10/235,401, filed on Sep. 5, 2002, which is a continuation of U.S. patent application Ser. No. 09/609,889, filed on Jul. 5, 2000, the contents of which are incorporated herein by reference. 
     
    
     TECHNICAL FIELD  
       [0002]     The present invention relates to synthesis-based pre-selection of suitable units for concatenative speech and, more particularly, to the utilization of a table containing many thousands of synthesized sentences for selecting units from a unit selection database.  
       BACKGROUND OF THE INVENTION  
       [0003]     A current approach to concatenative speech synthesis is to use a very large database for recorded speech that has been segmented and labeled with prosodic land spectral characteristics, such as the fundamental frequency (F 0 ) for voiced speech, the energy or gain of the signal, land the spectral distribution of the signal (i.e., how much of the signal is present at any given frequency). The database contains multiple instances of speech sounds. This multiplicity permits the possibility of having units in the database that are much less stylized than would occur in a diphone database (a “diphone” being defined as the second half of one phoneme followed by the initial half of the following phoneme, a diphone database generally containing only one instance of any given diphone). Therefore, the possibility of achieving natural speech is enhanced with the “large database” approach.  
         [0004]     For good quality synthesis, this database technique relies on being able to select the “best” units from the database—that is, the units that are closest in character to the prosodic specification provided by the speech synthesis system, land that have a low spectral mismatch at the concatenation points between phonemes. The “best” sequence of units may be determined by associating a numerical cost in two different ways. First, a “target cost” is associated with the individual units in isolation, where a lower cost is associated with a unit that has characteristics (e.g., F 0 , gain, spectral distribution) relative close to the unit being synthesized, and a higher cost is associated with units having a higher discrepancy with the unit being synthesized. A second cost, referred to as the “concatenation cost” is associated with how smoothly two contiguous units are joined together. For example, if the spectral mismatch between units is poor, there will be a higher concatenation cost.  
         [0005]     Thus a set of candidate units for each position in the desired sequence can be formulated, with associated target costs and concatenative costs. Estimating the best (lowest-cost) path through the network is then performed using, for example, a Viterbi search. The chosen units may then concatenated to form one continuous signal, using a variety of different techniques.  
         [0006]     While such database-driven systems may produce a more natural sounding voice quality to do so they require a great deal of computational resources during the synthesis process. Accordingly, there remains a need for new methods and systems that provide natural voice quality in speech synthesis while reducing the computational requirements.  
       SUMMARY OF THE INVENTION  
       [0007]     The need remaining in the prior art is addressed by the present invention, which relates to synthesis-based pre-selection of suitable units for concatenative speech and more particularly, to the utilization of a table containing many thousands of synthesized sentences as a guide to selecting units from a unit selection database.  
         [0008]     In accordance with the present invention, an extensive database of synthesized speech is created by synthesizing a large number of sentences (large enough to create millions of separate phonemes, for example). From this data, a set of all triphone sequences is then compiled, where a “triphone” is defined as a sequence of three phonemes—or a phoneme “triplet”. A list of units (phonemes) from the speech synthesis database that have been chosen for each context is then tabulated.  
         [0009]     During the actual text-to-speech synthesis process, the tabulated list is then reviewed for the proper context and these units (phonemes) become the candidate units for synthesis. A conventional cost algorithm, such as a Viterbi search, can then be used to ascertain the best choices from the candidate list for the speech output. If a particular unit to be synthesized does not appear in the created table, a conventional speech synthesis process can be used, but this should be a rare occurrence.  
         [0010]     Other and further aspects of the present invention will become apparent during the course of the following discussion and by reference to the accompanying drawings.  
     
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0011]     Referring now to the drawings,  
         [0012]      FIG. 1  illustrates an exemplary speech synthesis system for utilizing the triphone selection arrangement of the present invention;  
         [0013]      FIG. 2  illustrates, in more detail, an exemplary text-to-speech synthesizer that may be used in the system of  FIG. 1 ;  
         [0014]      FIG. 3  is a flowchart illustrating the creation of the unit selection database of the present invention; and  
         [0015]      FIG. 4  is a flowchart illustrating an exemplary unit (phoneme) selection process using the unit selection database of the present invention. 
     
    
     DETAILED DESCRIPTION  
       [0016]     An exemplary speech synthesis system  100  is illustrated in  FIG. 1 . System  100  includes a text-to-speech synthesizer  104  that is connected to a data source  102  through an input link  108 , and is similarly connected to a data sink  106  through an output link  110 . Text-to-speech synthesizer  104 , as discussed in detail below in association with  FIG. 2 , functions to convert the text data either to speech data or physical speech. In operation, synthesizer  104  converts the text data by first converting the text into a stream of phonemes representing the speech equivalent of the text, then processes the phoneme stream to produce to an acoustic unit stream representing a clearer and more understandable speech representation. Synthesizer  104  then converts the acoustic unit stream to speech data or physical speech.  
         [0017]     Data source  102  provides text-to-speech synthesizer  104 , via input link  108 , the data that represents the text to be synthesized. The data representing the text of the speech can be in any format, such as binary, ASCII, or a word processing file. Data source  102  can be any one of a number of different types of data sources, such as a computer, a storage device, or any combination of software and hardware capable of generating, relaying, or recalling from storage, a textual message or any information capable of being translated into speech. Data sink  106  receives the synthesized speech from text-to-speech synthesizer  104  via output link  110 . Data sink  106  can be any device capable of audibly outputting speech, such as a speaker system for transmitting mechanical sound waves, or a digital computer, or any combination or hardware and software capable of receiving, relaying, storing, sensing or perceiving speech sound or information representing speech sounds.  
         [0018]     Links  108  and  110  can be any suitable device or system for connecting data source  102 /data sink  106  to synthesizer  104 . Such devices include a direct serial/parallel cable connection, a connection over a wide area network (WAN) or a local area network (LAN), a connection over an intranet, the Internet, or any other distributed processing network or system. Additionally, input link  108  or output link  110  may be software devices linking various software systems.  
         [0019]      FIG. 2  contains a more detailed block diagram of text-to-speech synthesizer  104  of  FIG. 1 . Synthesizer  104  comprises, in this exemplary embodiment, a text normalization device  202 , syntactic parser device  204 , word pronunciation module  206 . prosody generation device  208 , an acoustic unit selection device  210 , and a speech synthesis back-end device  212 . In operation, textual data is received on input link  108  and first applied as an input to text normalization device  202 . Text normalization device  202  parses the text data into known words and further converts abbreviations and numbers into words to produce a corresponding set of normalized textual data. For example, if “St.” is input, text normalization device  202  is used to pronounce the abbreviation as either “saint” or “street”, but not the /st/ sound. Once the text has been normalized, it is input to syntactic parser  204 . Syntactic processor  204  performs grammatical analysis of a sentence to identify the syntactic structure of each constituent phrase and word. For example, syntactic parser  204  will identify a particular phrase as a “noun phrase” or a “verb phrase” and a word as a noun, verb, adjective, etc. Syntactic parsing is important because whether the word or phrase is being used as a noun or a verb may affect how it is articulated. For example, in the sentence “the cat ran away”, if “cat” is identified as a noun and “ran” is identified as a verb, speech synthesizer  104  may assign the word “cat” a different sound duration and intonation pattern than “ran” because of its position and function in the sentence structure.  
         [0020]     Once the syntactic structure of the text has been determined, the text is input to word pronunciation module  206 . In word pronunciation module  206 , orthographic characters used in the normal text are mapped into the appropriate strings of phonetic segments representing units of sound and speech. This is important since the same orthographic strings may have different pronunciations depending on the word in which the string is used. For example, the orthographic string “gh” is translated to the phoneme /f/ in “tough”, to the phoneme /g/ in “ghost”, and is not directly realized as any phoneme in “though”. Lexical stress is also marked. For example, “record” has a primary stress on the first syllable if it is a noun, but has the primary stress on the second syllable if it is a verb. The output from word pronunciation module  206 , in the form of phonetic segments, is then applied as an input to prosody determination device  208 . Prosody determination device  208  assigns patterns of timing and intonation to the phonetic segment strings. The timing pattern includes the duration of sound for each of the phonemes. For example, the “re” in the verb “record” has a longer duration of sound than the “re” in the noun “record”. Furthermore, the intonation pattern concerns pitch changes during the course of an utterance. These pitch changes express accentuation of certain words or syllables as they are positioned in a sentence and help convey the meaning of the sentence. Thus, the patterns of timing and intonation are important for the intelligibility and naturalness of synthesized speech. Prosody may be generated in various ways including assigning an artificial accent or providing for sentence context. For example, the phrase “This is a test! ” will be spoken differently from “This is a test? ”. Prosody generating devices are well-known to those of ordinary skill in the art and any combination of hardware, software, firmware, heuristic techniques, databases, or any other apparatus or method that performs prosody generation may be used. In accordance with the present invention, the phonetic output from prosody determination device  208  is an amalgam of information about phonemes, their specified durations and F 0  values.  
         [0021]     The phoneme data, along with the corresponding characteristic parameters, is then sent to acoustic unit selection device  210 , where the phonemes and characteristic parameters are transformed into a stream of acoustic units that represent speech. An acoustic unit” can be defined as a particular utterance of a given phoneme. Large numbers of acoustic units may all correspond to a single phoneme, each acoustic unit differing from one another in terms of pitch, duration and stress (as well as other phonetic or prosodic qualities). In accordance with the present invention a triphone database  214  is accessed by unit selection device  210  to provide a candidate list of units that are most likely to be used in the synthesis process. In particular and as described in detail below, triphone database  214  comprises an indexed set of phonemes, as characterized by how they appear in various triphone contexts, where the universe of phonemes was created from a continuous stream of input speech. Unit selection device  210  then performs a search on this candidate list (using a Viterbi “least cost” search, or any other appropriate mechanism) to find the unit that best matches the phoneme to be synthesized. The acoustic unit output stream from unit selection device  210  is then sent to speech synthesis back-end device  212 , which converts the acoustic unit stream into speech data and transmits the speech data to data sink  106  (see  FIG. 1 ), over output link  110 .  
         [0022]     In accordance with the present invention, triphone database  214  as used by unit selection device  210  is created by first accepting an extensive collection of synthesized sentences that are compiled and stored.  FIG. 3  contains a flow chart illustrating an exemplary process for preparing unit selection triphone database  214 , beginning with the reception of the synthesized sentences (block  300 ). In one example, two weeks&#39; worth of speech was recorded and stored, accounting for 25 million different phonemes. Each phoneme unit is designated with a unique number in the database for retrieval purposes (block  310 ). The synthesized sentences are then reviewed and all possible triphone combinations identified (block  320 ). For example, the triphone/k/ /œ/ /t/(consisting of the phoneme /œ/and its immediate neighbors) may have many occurrences in the synthesized input. The list of unit numbers for each phoneme chosen in a particular context are then tabulated so that the triphones are later identifiable (block  330 ). The final database structure, therefore, contains sets of unit numbers associated with each particular context of each triphone likely to occur in any text that is to be later synthesized.  
         [0023]     An exemplary text to speech synthesis process using the unit selection database generated according to the present invention is illustrated in the flow chart of  FIG. 4 . The first step in the process is to receive the input text (block  410 ) and apply it as an input to text normalization device (block  420 ). The normalized text is then syntactically parsed (block  430 ) so that the syntactic structure of each constituent phrase or word is identified as, for example, a noun, verb, adjective, etc. The syntactically parsed text is then expressed as phonemes (block  440 ), where these phonemes (as well as information about their triphone context) are then applied as inputs to triphone selection database  214  to ascertain likely synthesis candidates (block  450 ). For example, if the sequence of phonemes /k/ /œ/ /t/ is to be synthesized, the unit numbers for a set of N phonemes /œ/ are selected from the database created as outlined above in  FIG. 3 , where N can be any relatively small number (e.g., 40-50). A candidate list of each of the requested phonemes are generated (block  460 ) and a Viterbi search is performed (block  470 ) to find the least cost path through the selected phonemes. The selected phonemes may be then be further processed (block  480 ) to form the actual speech output.