Source: http://www.google.de/patents/US8005677
Timestamp: 2013-12-13 11:10:34
Document Index: 440719797

Matched Legal Cases: ['Application No. 2', 'Application No. 04750993', 'Application No. 2', 'Application No. 2', 'application No. 2004238228', 'Application No. 200480010899']

Patent US8005677 - Source-dependent text-to-speech system - Google PatenteSuche Bilder Maps Play YouTube News Gmail Drive Mehr »Anmelden Erweiterte Patentsuche PatenteA method of generating speech from text messages includes determining a speech feature vector for a voice associated with a source of a text message, and comparing the speech feature vector to speaker models. The method also includes selecting one of the speaker models as a preferred match for the voice...http://www.google.de/patents/US8005677?utm_source=gb-gplus-sharePatent US8005677 - Source-dependent text-to-speech system Erweiterte Patentsuche Ver�ffentlichungsnummerUS8005677 B2PublikationstypErteilung AnmeldenummerUS 10/434,683 Ver�ffentlichungsdatum23. Aug. 2011Eingetragen9. Mai 2003 Priorit�tsdatum9. Mai 2003Auch ver�ffentlicht unterCA2521440A1, CA2521440C, CN1894739A, CN1894739B, EP1623409A2, EP1623409A4, US20040225501, WO2004100638A2, WO2004100638A3 Ver�ffentlichungsnummer10434683, 434683, US 8005677 B2, US 8005677B2, US-B2-8005677, US8005677 B2, US8005677B2 ErfinderNicholas J. CutaiaUrspr�nglich Bevollm�chtigterCisco Technology, Inc.Zitat exportierenBiBTeX, EndNote, RefManPatentzitate (24), Nichtpatentzitate (17), Referenziert von (3), Klassifizierungen (8), Juristische Ereignisse (1) Externe Links: USPTO, USPTO-Zuordnung, EspacenetSource-dependent text-to-speech systemUS 8005677 B2 Zusammenfassung A method of generating speech from text messages includes determining a speech feature vector for a voice associated with a source of a text message, and comparing the speech feature vector to speaker models. The method also includes selecting one of the speaker models as a preferred match for the voice based on the comparison, and generating speech from the text message based on the selected speaker model.
means for analyzing the model voice samples to determine the speaker model for each model voice sample. Beschreibung
TECHNICAL FIELD OF THE INVENTION This invention relates in general to text-to-speech systems, and more particularly to a source-dependent text-to-speech system.
BACKGROUND OF THE INVENTION Text-to-speech (TTS) systems provide versatility in telecommunications networks. TTS systems produce audible speech from text messages, such as email, instant messages, or other suitable text. One drawback of TTS systems is that the voice produced by the TTS system is often generic and not associated with the particular source providing the message. For example, a text-to-speech system may produce a male voice no matter who the person sending the message is, making it difficult to tell whether a particular message came from a man or a woman.
SUMMARY OF THE INVENTION In accordance with the present invention, a text-to-speech system provides a source-dependent rendering of text messages in a voice similar to the person providing the message. This increases the ability of a user of TTS systems to determine the source of a text message by associating the message with the sound of a particular voice. In particular, certain embodiments of the present invention provide a source-dependent TTS system.
DETAILED DESCRIPTION OF THE INVENTION FIG. 1 shows a telecommunications network 100 that allows endpoints 108 to exchange information with one another in the form of text and/or voice messages. In general, components of network 100 embody techniques for generating voice messages from text messages such that the acoustic characteristics of the voice message correspond to the acoustic characteristics of a voice associated with a source of the text message. In the depicted embodiment, network 100 includes data networks 102 coupled to the public switched telephone network (PSTN) 104 by a gateway 106. Endpoints 108 coupled to networks 102 and 104 provide communication services to users. Various servers in network 100 provide services to endpoints 108. In particular, network 100 includes a speech feature vector (SFV) server 200, a voice match server 300, a text-to-speech (TTS) server 400, and a unified messaging server 110. In alternative embodiments, the functions and services provided by various components may be aggregated within or distributed among different or additional components, including examples such as integrating servers 200, 300, and 400 into a single server or providing a distributed architecture in which endpoints 108 perform the described functions of servers 200, 300, and 400.
Network 100 applies pattern recognition techniques to voice by computing speech feature vectors. As used in the following description, �speech feature vector� refers to any of a number of mathematical quantities that describe speech. Initially, network 100 computes speech feature vectors for a range of voices that may be generated by a TTS system, and associates the speech feature vectors for each voice with settings of the TTS system used the generate the voice. In the following description, such settings of the TTS system are referred to as �TTS markup parameters.� Once the voices of the TTS system are learned, network 100 uses pattern recognition to compare new voices to stored voices. The comparison between voices may involve a basic comparison of numerical values or may involve more complex techniques, such as hypothesis-testing, in which the voice recognition system uses any of several techniques to identify potential matches for a voice under consideration and computes a probability score that the voices match. Furthermore, optimization techniques, such as gradient descent or conjugate gradient descent, may be used to select candidates. Using such comparison techniques, a voice recognition system can determine a preferred match among stored voices to a new voice, and in turn may associate the new voice with a set of TTS markup parameters. The following description describes embodiments of these and similar techniques and the manner in which components of the depicted embodiment of network 100 may perform these functions.
In the depicted embodiment of network 100, networks 102 represent any hardware and/or software for communicating voice and/or data information among components in the form of packets, frames, cells, segments, or other portions of data (generally referred to as �packets�). Network 102 may include any combination of routers, switches, hubs, gateways, links, and other suitable hardware and/or software components. Network 102 may use any suitable protocol or medium for carrying information, including Internet protocol (IP), asynchronous transfer mode (ATM), synchronous optical network (SONET), Ethernet, or any other suitable communication medium or protocol.
In a particular embodiment, SFV server 200 computes speech feature vectors for an adapted Gaussian mixture model (GMM), such as those described in the article �Speaker Verification Using Adapted Gaussian Mixture Models,� by Douglas A. Reynolds, Thomas F. Quatieri, and Robert B. Dunn and �Robust Text-Independent Speaker Identification Using Gaussian Mixture Speaker Models� by Douglas A. Reynolds and Richard C. Rose. In this particular embodiment of Gaussian mixture model analysis, speech feature vectors are computed by determining the spectral energy of logarithmically-spaced filters with increasing bandwidths (�mel-filters�). The discrete cosine transform of the log-spectral energy thus obtained is known as the �mel-scale cepstrum� of the speech. The coefficients of terms in the mel-scale cepstrum, known as �feature vectors,� are normalized to remove linear channel convolutional effects (additive biases) and to calculate uncertainty ranges (�delta cepstra�) for the feature vectors. For example, additive biases may be removed by cepstral mean subtraction (CMS) and/or relative spectral (RASTA) processing. Delta cepstra may be calculated using techniques such as fitting a polynomial over a range of adjacent feature vectors. The resulting feature vectors characterize the sound, and may be compared to other sounds using various statistical analysis techniques.
Voice match server 300 represents any suitable hardware and/or software for comparing measured parameter sets to speaker models and determining a preferred match between the measured speech feature vectors and a speaker model. �Speaker model� refers to any mathematical quantity or set of quantities that describes a voice produced by a text-to-speech device or algorithm. Speaker models may be chosen to coincide with the type of speech feature vectors determined by SFV server 200 in order to facilitate comparison between speaker models and measured speech feature vectors, and they may be stored or, alternatively, produced in response to a particular text message, voice sample, or other source. Voice match server 300 may employ any suitable technique, method, or algorithm for comparing measured speech feature vectors to speaker models. For example, voice match server 300 may match speech characteristics using a likelihood function, such as the log-likelihood function of Gaussian mixture models or the more complex likelihood function of hidden Markov models. In a particular embodiment, voice match server 300 uses Gaussian mixture models to compare measured parameters with voice models.
Various other techniques of speech analysis may also be employed. For example, long-term averaging of acoustic features, such as spectrum representation or pitch, can reveal unique characteristics of speech by removing phonetic variations and other short-term speech effects that may make it difficult to identify the speaker. Other techniques involve comparing phonetic sounds based on similar texts to identify distinguishing characteristics of voices. Such techniques may use hidden Markov models (HMMs) to analyze the difference between similar phonemes by taking into account underlying relationships between the phonemes (�Markovian connections�). Alternative techniques may include training recognition algorithms in a neural network, so that the recognition algorithm used may vary depending on the particular speakers for which the network is trained. Network 100 may be adapted to use any of the described techniques or any other suitable technique for using measured speech feature vectors to compute a score for each of a group of candidate speaker models and determining a preferred match between the measured speech feature vectors and one of the speaker models. �Speaker models� refer to any mathematical quantities that characterize a voice associated with a particular set of TTS markup parameters and that are used in hypothesis-testing the measured speech vectors for a preferred match. For example, for Gaussian mixture models, speaker models may include the number of Gaussians in the mixture density function, the set of N probability weights, the set of N mean vectors for each of the member Gaussian densities, and the set of N covariance matrices for each of the member Gaussian densities.
In operation, a sending endpoint 108 a communicates a text message to a receiving endpoint 108 b. Receiving endpoint 108 b may be set in a text-to-speech mode so that it outputs text messages as speech. In that case, components of network 100 determine a set of speech feature vectors for a voice associated with the source of a text message. The �source� of a text message may refer to endpoint 108 a or other component that generated the message, and may also refer to the user of such a device. Thus, for example, a voice associated with the source of a text message may be the voice of a user of endpoint 108 a. Network 100 compares the set of speech feature vectors to the speaker models to select a preferred match, which refers to a speaker model deemed to be the preferred match for the set of speech feature vectors of the voice by whatever comparison test is used. Network 100 then generates speech based on TTS markup parameters associated with the speaker model chosen as the preferred match.
Patentzitate Zitiertes PatentEingetragen Ver�ffentlichungsdatum Antragsteller TitelUS5704007 *4. Okt. 199630. Dez. 1997Apple Computer, Inc.Utilization of multiple voice sources in a speech synthesizerUS5913193 *30. Apr. 199615. Juni 1999Microsoft CorporationMethod and system of runtime acoustic unit selection for speech synthesisUS5915237 *13. Dez. 199622. Juni 1999Intel CorporationRepresenting speech using MIDIUS628908516. Juni 199811. Sept. 2001International Business Machines CorporationVoice mail system, voice synthesizing device and method thereforUS64249465. Nov. 199923. Juli 2002International Business Machines CorporationMethods and apparatus for unknown speaker labeling using concurrent speech recognition, segmentation, classification and clusteringUS6539354 *24. M�rz 200025. M�rz 2003Fluent Speech Technologies, Inc.Methods and devices for producing and using synthetic visual speech based on natural coarticulationUS6651042 *31. Aug. 200018. Nov. 2003International Business Machines CorporationSystem and method for automatic voice message processingUS6813604 *13. Nov. 20002. Nov. 2004Lucent Technologies Inc.Methods and apparatus for speaker specific durational adaptationUS6873952 *16. Mai 200329. M�rz 2005Tellme Networks, Inc.Coarticulated concatenated speechUS6970820 *26. Febr. 200129. Nov. 2005Matsushita Electric Industrial Co., Ltd.Voice personalization of speech synthesizerUS7177801 *21. Dez. 200113. Febr. 2007Texas Instruments IncorporatedSpeech transfer over packet networks using very low digital data bandwidthsUS7200560 *19. Nov. 20023. Apr. 2007Medaline Elizabeth PhilbertPortable reading device with display capabilityUS2001005634810. Jan. 200027. Dez. 2001Henry C A Hyde-ThomsonUnified Messaging System With Automatic Language Identification For Text-To-Speech ConversionUS2002010364827. M�rz 20011. Aug. 2002Case Eliot M.System and method for converting text-to-voiceUS2002014354229. M�rz 20013. Okt. 2002Ibm CorporationTraining of text-to-speech systemsUS200201696105. Apr. 200214. Nov. 2002Volker LueggerMethod and system for automatically converting text messages into voice messagesUS2002019399430. M�rz 200119. Dez. 2002Nicholas KibreText selection and recording by feedback and adaptation for development of personalized text-to-speech systemsGB2364850A Titel nicht verf�gbarJP2000148189A Titel nicht verf�gbarJPH07319495A Titel nicht verf�gbarJPS6128128A Titel nicht verf�gbarWO2002011016A29. Juli 20017. Febr. 2002Thomas M CroftSystem and method for personalizing electronic mail messagesWO2002049003A111. Dez. 200120. Juni 2002Siemens AgMethod and system for converting text to speechWO2002090915A125. Apr. 200214. Nov. 2002Koninkl Philips Electronics NvBackground learning of speaker voices* Vom Pr�fer zitiertNichtpatentzitateReferenz1"Speech Processing, Transmission and Quality aspects (STQ); Distributed Speech Recognition; Front-end feature extraction algorithm; Compression algorithms" ETSI ES 201108 V1.1.2 (Apr. 2000) ETSI Standard, European Telecommunications Standards Institute, Oct. 30, 2002, 20 pages.2Burger et al., "Requirements for Distributed Control of ASR, SI/SV and TTS Resources," Internet Draft, The Internet Society, Dec. 6, 2002, 19 pages.3Canadian Intellectual Property Office Examination Report; Application No. 2,521,440; Title: Source-Dependent Text-to-Speech System, Apr. 17, 2009.4 *English Translation of Juan Dafcik et al. WO 02/49003 "Method and System for Converting Text to Speech", translated Aug. 29, 2007 by Martha Witebsky, Translations Branch, USPTO.5European Search Report under Article 157(2)(a) EPC regarding Application No. 04750993.0-2218 (PCT/US2004013366), Dec. 12, 2006.6First Office Action issued by the State Intellectual Property Office of the People's Republic of China; Filing No. 200480010899.X; Title of Invention: Source-Dependent Text-to-Speech System, Apr. 10, 2009.7 *Kain et al. Spectral Voice Conversion for Text-To-Speech Synthesis, May 12-15, 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 285-288.8Notification of Transmittal of the International Search Report and the Written Opinion of the International Searching Authority for International application No. PCT/US04/13366, filed Apr. 28, 2004, (10 pages), Mar. 14, 2006.9Office Action issued by the Canadian Intellectual Property Office; Application No. 2,521,440; Owner: Cisco Technology, Inc.; Title: Source-Dependent Text-to-Speech System Apr. 11, 2011.10Office Action issued by the Canadian Intellectual Property Office; Application No. 2,521,440; Owner: Cisco Technology, Inc.; Title: Source-Dependent Text-to-Speech System, Mar. 24, 2010.11Pizzey's, Australia, "Examiner's first report on patent application No. 2004238228;" reply to the request for examination; Reference No. 18493CIS/MRR:kj, 2 pages, Jan. 6, 2009.12Reynolds et al., "Speaker Verification Using Adapted Gaussian Mixture Models," Digital Signal Processing Review Journal, vol. 10, 2000, 21 pages.13Reynolds et al., Robust Text-Independent Speaker Identification Using Gaussian Mixture Speaker Models, IEEE Transactions on Speech and Audio Processing, vol. 3, No. 1, Jan. 1995, pp. 72-83.14Shanmugham et al., "MRCP: Media Resource Control Protocol," Internet Engineering Task Force Internet Draft, The Internet Society, Jan. 24, 2003, 76 pages.15Speech Synthesis Markup Language Version 1.0-W3C Working Draft, W3C, Dec. 2, 2002, 38 pages.16Speech Synthesis Markup Language Version 1.0�W3C Working Draft, W3C, Dec. 2, 2002, 38 pages.17The Second Office Action issued by The Patent Office of the People's Republic of China; Application No. 200480010899.X, Date of Issue, Sep. 25, 2009.* Vom Pr�fer zitiert Referenziert von Zitiert von PatentEingetragen Ver�ffentlichungsdatum Antragsteller TitelUS8285548 *9. M�rz 20099. Okt. 2012Lg Electronics Inc.Communication device processing text message to transform it into speechUS85101144. Okt. 201213. Aug. 2013Lg Electronics Inc.Communication device transforming text message into speechUS20090228278 *9. M�rz 200910. Sept. 2009Ji Young HuhCommunication device and method of processing text message in the communication device* Vom Pr�fer zitiertKlassifizierungen US-Klassifikation704/260Internationale KlassifikationG10L13/08, G10L13/04, G10L13/00, G10L13/02 UnternehmensklassifikationG10L13/047, G10L13/033 Europ�ische KlassifikationG10L13/033Juristische Ereignisse DatumCodeEreignisBeschreibung9. Mai 2003ASAssignmentOwner name: CISCO TECHNOLOGY, INC., CALIFORNIAFree format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:CUTAIA, NICHOLAS J.;REEL/FRAME:014062/0012Effective date: 20030508DrehenOriginalbildGoogle-Startseite - Sitemap - USPTO-Bulk-Downloads - Datenschutzerkl�rung - Nutzungsbedingungen - �ber Google Patente - Feedback gebenDaten bereitgestellt von IFI CLAIMS Patent Services.© 2012 Google